As the quality of contemporary speech synthesis improves, so too does the interest from language communities in developing text-to-speech (TTS) systems for a variety of real-world applications. Much of the work on TTS has focused on high-resource languages, resulting in implicitly resource-intensive paths to building such systems. The goal of this paper is to provide signposts and points of reference for future low-resource speech synthesis efforts, with insights drawn from the Speech Generation for Indigenous Language Education (SGILE) project. Funded and coordinated by the National Research Council of Canada (NRC), this multi-year, multi-partner project has the goal of producing high-quality text-to-speech systems that support the teaching of Indigenous languages in a variety of educational contexts. We provide background information and motivation for the project, as well as details about our approach and project structure, including results from a multi-day requirements-gathering session. We discuss some of our key challenges, including building models with appropriate controls for educators, improving model data efficiency, and strategies for low-resource transfer learning and evaluation. Finally, we provide a detailed survey of existing speech synthesis software and introduce EveryVoice TTS, a toolkit designed specifically for low-resource speech synthesis. • We provide background and points of reference for future low-resource TTS projects • We describe four main technical challenges for low-resource speech synthesis • We introduce the EveryVoice TTS Toolkit designed specifically for low-resource TTS • We compare the EveryVoice TTS Toolkit with six other existing toolkits

Mean Opinion Score (MOS) tests are the most widely used test type for subjective evaluation of speech samples. However, their use has been questioned, as results can vary significantly depending on the test material included. Forced-choice tests such as AB or Best Worst Scaling (BWS) can in principle mitigate some of these issues. Our aim here is to compare MOS, AB and BWS tests in 3 regards: 1) Which test type do listeners prefer in terms of ease, engagement and overall likeability? 2) How fast are listeners at each test type? 3) Does each test type provide the same pattern of results? To answer these questions we re-use a subset of stimuli from the Blizzard Challenge 2013 and conduct new MOS, AB and BWS tests. Overall, we conclude each test type is broadly equally valid, MOS may not in fact be the fastest or easiest test type for listeners, but the theoretical advantages of BWS are counterbalanced by it seeming less liked by our listeners here.

We propose a new method for human speech intelligibility evaluation based on keyword spotting. In this method, participants play a stimulus and select the word they hear from a close set of alternatives. To find which sentence to use, the target word, and alternatives we mine a large set of stimuli using a phonetic dictionary and a language model. Unlike other tests, our method does not rely on specially designed sentences and can be used to evaluate in-the-wild material such as TED talks. We focus on audio-visual (AV) speech enhancement (SE) evaluation as a study case. We compared our method to a transcription task and observed that the two produce highly correlated results, albeit our task requiring substantially less participation time. We then adopted it on a large-scale evaluation of AVSE systems. Results show that keyword spotting is a suitable and efficient alternative to assess intelligibility from AV stimuli.

We present a neural vocoder designed with low-powered Alternative and Augmentative Communication devices in mind. By combining elements of successful modern vocoders with established ideas from an older generation of technology, our system is able to produce high quality synthetic speech at 48kHz on devices where neural vocoders are otherwise prohibitively complex. The system is trained adversarially using differentiable pitch synchronous overlap add, and reduces complexity by relying on pitch synchronous Inverse Short-Time Fourier Transform (ISTFT) to generate speech samples. Our system achieves comparable quality with a strong baseline (HiFi-GAN) while using only a fraction of the compute. We present results of a perceptual evaluation as well as an analysis of system complexity.

Most state-of-the-art Text-to-Speech systems use the mel-spectrogram as an intermediate representation, to decompose the task into acoustic modelling and waveform generation.A mel-spectrogram is extracted from the waveform by a simple, fast DSP operation, but generating a high-quality waveform from a mel-spectrogram requires computationally expensive machine learning: a neural vocoder. Our proposed "autovocoder" reverses this arrangement. We use machine learning to obtain a representation that replaces the mel-spectrogram, and that can be inverted back to a waveform using simple, fast operations including a differentiable implementation of the inverse STFT.The autovocoder generates a waveform 5 times faster than the DSP-based Griffin-Lim algorithm, and 14 times faster than the neural vocoder HiFi-GAN. We provide perceptual listening test results to confirm that the speech is of comparable quality to HiFi-GAN in the copy synthesis task.

Automatically predicting the outcome of subjective listening tests is a challenging task. Ratings may vary from person to person even if preferences are consistent across listeners. While previous work has focused on predicting listeners' ratings (mean opinion scores) of individual stimuli, we focus on the simpler task of predicting subjective preference given two speech stimuli for the same text. We propose a model based on anti-symmetric twin neural networks, trained on pairs of waveforms and their corresponding preference scores. We explore both attention and recurrent neural nets to account for the fact that stimuli in a pair are not time aligned. To obtain a large training set we convert listeners' ratings from MUSHRA tests to values that reflect how often one stimulus in the pair was rated higher than the other. Specifically, we evaluate performance on data obtained from twelve MUSHRA evaluations conducted over five years, containing different TTS systems, built from data of different speakers. Our results compare favourably to a state-of-the-art model trained to predict MOS scores.

Audio-visual speech enhancement is the task of improving the quality of a speech signal when video of the speaker is available. It opens-up the opportunity of improving speech intelligibility in adverse listening scenarios that are currently too challenging for audio-only speech enhancement models. The Audio-Visual Speech Enhancement (AVSE) challenge aims to set the first benchmark in this area. We provide participants with datasets and scripts to test their audio-visual speech enhancement models under a common framework for both training and evaluation. The data is derived from real-world videos, and comprises noisy mixes, in which audio from target speaker is mixed with either a competing speaker or a noise signal. The submitted systems are evaluated by conducting AV intelligibility tests involving human participants. We expect this challenge to be a platform for advancing the field of audio-visual speech-enhancement and to provide further insight about the scope and limitations of current AV speech enhancement approaches.

Sequence-to-sequence speech synthesis models are notorious for gross errors such as skipping and repetition, commonly associated with failures in the attention mechanism. While a lot has been done to improve attention and decrease errors, this paper focuses instead on automatic error detection and analysis. We evaluated three objective metrics against error detection scores collected by human listening. All metrics were derived from the synthesised attention matrix alone and do not require a reference signal, relying on the expectation that errors occur when attention is dispersed or insufficient. Using one of this metrics as an analysis tool, we observed that gross errors are more likely to occur in longer sentences and in sentences with punctuation marks that indicate pause or break. We also found that mechanisms such as forcibly incremented attention have the potential for decreasing gross errors but to the detriment of naturalness. The results of the error detection evaluation revealed that two of the evaluated metrics were able to detect errors with a relatively high success rate, obtaining F-scores of up to 0.89 and 0.96.

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This paper presents a method for upsampling and transforming a compact representation of acoustics into a corresponding speech waveform. Similar to a conventional vocoder, the proposed system takes a pulse train derived from fundamental frequency and a noise sequence as inputs and shapes them to be consistent with the acoustic features. However, the filters that are used to shape the waveform in the proposed system are learned from data, and take the form of layers in a convolutional neural network. Because the network performs the transformation simultaneously for all waveform samples in a sentence, its synthesis speed is comparable with that of conventional vocoders on CPU, and many times faster on GPU. It is trained directly in a fast and straightforward manner, using a combined time- and frequency-domain objective function. We use publicly available data and provide code to allow our results to be reproduced.

Speech playback (e.g., TV, radio, public address) becomes harder to understand in the presence of noise and reverberation.\nNELE (Near End Listening Enhancement) algorithms can improve intelligibility by modifying the signal before it is played back. Substantial intelligibility improvements have been achieved in the lab for both natural and synthetic speech. However, evidence is still scarce on how these algorithms work under conditions of realistic noise and reverberation.\n\nWe present a realistic test platform, featuring two representative everyday scenarios in which speech playback may occur (in the presence of both noise and reverberation): a domestic space (living room) and a public space (cafeteria). The generated stimuli are evaluated by measuring keyword accuracy rates in a listening test with normal hearing subjects.\n\nWe use the new platform to compare three state-of-theart NELE algorithms, employing either noise-adaptive or nonadaptive\nstrategies, and with or without compensation for reverberation.

This paper presents a simple but effective method for generating speech waveforms by selecting small units of stored speech to match a low-dimensional target representation. The method is designed as a drop-in replacement for the vocoder in a deep neural network-based text-to-speech system. Most previous work on hybrid unit selection waveform generation relies on phonetic annotation for determining unit boundaries, or for specifying target cost, or for candidate preselection. In contrast, our waveform generator requires no phonetic information, annotation, or alignment. Unit boundaries are determined by epochs, and spectral analysis provides representations which are compared directly with target features at runtime. As in unit selection, we minimise a combination of target cost and join cost, but find that greedy left-to-right nearest-neighbour search gives similar results to dynamic programming. The method is fast and can generate the waveform incrementally. We use publicly available data and provide a permissively-licensed open source toolkit for reproducing our results.

This paper presents a hybrid text-to-speech framework that uses a waveform generation method based on examplars of natural speech waveform. These examplars are selected at synthesis time given a sequence of acoustic features generated from text by a statistical parametric speech synthesis model. In order to match the expected degradation of these target synthesis features, the database of units is constructed such that the units' target representations are generated from the same parametric model. We evaluate two variants of this framework by modifying the size of the examplar: a small unit variant (where unit boundaries are determined by pitch mark location) and a halfphone variant (where unit boundaries are determined by subphone state forced alignment). We found that for a larger dataset (around four hours of training data) the examplar-based waveform generation variants are rated higher than the vocoder-based system.

Text-to-speech voices created from noisy and reverberant recordings are of lower quality. A simple way to improve this is to increase the quality of the recordings prior to text-to-speech training with speech enhancement methods such as noise suppression and dereverberation. In this paper, we opted for this approach and to perform the enhancement, we used a recurrent neural network. The network is trained with parallel data of clean and lower quality recordings of speech. The lower quality data was artificially created by adding recordings of environmental noise to studio-quality recordings of speech and by convolving room impulse responses with these clean recordings. We trained separate networks with noise-only, reverberation-only, and both reverberation and additive noise data. The quality of voices trained with lower quality data that has been enhanced using these networks was significantly higher in all cases. For the noise-only case, the enhanced synthetic voice ranked as high as the voice trained with clean data. For the most realistic and challenging scenario, when both noise and reverberation were present, the improvements were more modest, but still significant.

We propose a simple new representation for the FFT spectrum tailored to statistical parametric speech synthesis. It consists of four feature streams that describe magnitude, phase and fundamental frequency using real numbers. The proposed feature extraction method does not attempt to decompose the speech structure (e.g., into source+filter or harmonics+noise). By avoiding the simplifications inherent in decomposition, we can dramatically reduce the “phasiness” and “buzziness” typical of most vocoders. The method uses simple and computationally cheap operations and can operate at a lower frame rate than the 200 frames-per-second typical in many systems. It avoids heuristics and methods requiring approximate or iterative solutions, including phase unwrapping. Two DNN-based acoustic models were built - from male and female speech data - using the Merlin toolkit. Subjective comparisons were made with a state-of-the-art baseline, using the STRAIGHT vocoder. In all variants tested, and for both male and female voices, the proposed method substantially outperformed the baseline. We provide source code to enable our complete system to be replicated.

This paper analyzes a) how often listeners interpret the emotional content of an utterance incorrectly when listening to vocoded or natural speech in adverse conditions; b) which noise conditions cause the most misperceptions; and c) which group of listeners misinterpret emotions the most. The long-term goal is to construct new emotional speech synthesizers that adapt to the environment and to the listener. We performed a large-scale listening test where over 400 listeners between the ages of 21 and 72 assessed natural and vocoded acted emotional speech stimuli. The stimuli had been artificially degraded using a room impulse response recorded in a car and various in-car noise types recorded in a real car. Experimental results show that the recognition rates for emotions and perceived emotional strength degrade as signal-to-noise ratio decreases. Interestingly, misperceptions seem to be more pronounced for negative and lowarousal emotions such as calmness or anger, while positive emotions such as happiness appear to be more robust to noise. An ANOVA analysis of listener meta-data further revealed that gender and age also influenced results, with elderly male listeners most likely to incorrectly identify emotions.

Intelligibility of speech in noise becomes lower as the listeners age increases, even when no apparent hearing impairment is present. The losses are, however, different depending on the nature of the noise and the characteristics of the voice. In this paper we investigate the effect that age, noise type and speaking style have on the intelligibility of speech reproduced by car loudspeakers. Using a binaural mannequin we recorded a variety of voices and speaking styles played from the audio system of a car while driving in different conditions. We used this material to create a listening test where participants were asked to transcribe what they could hear and recruited groups of young and older adults to take part in it. We found that intelligibility scores of older participants were lower for the competing speaker and background music conditions. Results also indicate that clear and Lombard speech was more intelligible than plain speech for both age groups. A mixed effect model revealed that the largest effect was the noise condition, followed by sentence type, speaking style, voice, age group and pure tone average.

Forced alignment for speech synthesis traditionally aligns a phoneme sequence predetermined by the front-end text processing system. This sequence is not altered during alignment, i.e., it is forced, despite possibly being faulty. The consistency assumption is the assumption that these mistakes do not degrade models, as long as the mistakes are consistent across training and synthesis. We present evidence that in the alignment of both standard read prompts and spontaneous speech this phoneme sequence is often wrong, and that this is likely to have a negative impact on acoustic models. A lattice-based forced alignment system allowing for pronunciation variation is implemented, resulting in improved phoneme identity accuracy for both types of speech. A perceptual evaluation of HMM-based voices showed that spontaneous models trained on this improved alignment also improved standard synthesis, despite breaking the consistency assumption.

We propose a new paradigm of waveform generation for Statistical Parametric Speech Synthesis that is based on neither source-filter separation nor sinusoidal modelling. We suggest that one of the main problems of current vocoding techniques is that they perform an extreme decomposition of the speech signal into source and filter, which is an underlying cause of “buzziness”, “musical artifacts”, or “ muffled sound” in the synthetic speech. The proposed method avoids making unnecessary assumptions and decompositions as far as possible, and uses only the spectral envelope and F0 as parameters. Prerecorded speech is used as a base signal, which is “reshaped” to match the acoustic specification predicted by the statistical model, without any source-filter decomposition. A detailed description of the method is presented, including implementation details and adjustments. Subjective listening test evaluations of complete DNN-based text-to-speech systems were conducted for two voices: one female and one male. The results show that the proposed method tends to outperform the state-of-theart standard vocoder STRAIGHT, whilst using fewer acoustic parameters.

Quality of text-to-speech voices built from noisy recordings is diminished. In order to improve it we propose the use of a recurrent neural network to enhance acoustic parameters prior to training. We trained a deep recurrent neural network using a parallel database of noisy and clean acoustics parameters as input and output of the network. The database consisted of multiple speakers and diverse noise conditions. We investigated using text-derived features as an additional input of the network. We processed a noisy database of two other speakers using this network and used its output to train an HMM acoustic text-to-synthesis model for each voice. Listening experiment results showed that the voice built with enhanced parameters was ranked significantly higher than the ones trained with noisy speech and speech that has been enhanced using a conventional enhancement system. The text-derived features improved results only for the female voice, where it was ranked as highly as a voice trained with clean speech.

This paper introduces a novel method for blind speech segmentation at a phone level based on image processing. We consider the spectrogram of the waveform of an utterance as an image and hypothesize that its striping defects, i.e. discontinuities, appear due to phone boundaries. Using a simple image destriping algorithm these discontinuities are found. To discover phone transitions which are not as salient in the image, we compute spectral changes derived from the time evolution of Mel cepstral parametrisation of speech. These socalled image-based and acoustic features are then combined to form a mixed probability function, whose values indicate the likelihood of a phone boundary being located at the corresponding time frame. The method is completely unsupervised and achieves an accuracy of 75.59% at a -3.26% over-segmentation rate, yielding an F-measure of 0.76 and an 0.80 R-value on the TIMIT dataset.

Given a transcription, sampling from a good model of acoustic feature trajectories should result in plausible realizations of an utterance. However, samples from current probabilistic speech synthesis systems result in low quality synthetic speech. Henter et al. have demonstrated the need to capture the dependencies between acoustic features conditioned on the phonetic labels in order to obtain high quality synthetic speech. These dependencies are often ignored in neural network based acoustic models. We tackle this deficiency by introducing a probabilistic neural network model of acoustic trajectories, trajectory RNADE, able to capture these dependencies.

Deep neural networks (DNNs) use a cascade of hidden representations to enable the learning of complex mappings from input to output features. They are able to learn the complex mapping from text-based linguistic features to speech acoustic features, and so perform text-to-speech synthesis. Recent results suggest that DNNs can produce more natural synthetic speech than conventional HMM-based statistical parametric systems. In this paper, we show that the hidden representation used within a DNN can be improved through the use of Multi-Task Learning, and that stacking multiple frames of hidden layer activations (stacked bottleneck features) also leads to improvements. Experimental results confirmed the effectiveness of the proposed methods, and in listening tests we find that stacked bottleneck features in particular offer a significant improvement over both a baseline DNN and a benchmark HMM system.

In this paper we evaluate how speaker familiarity influences the engagement times and performance of blind school children when playing audio games made with different synthetic voices. We developed synthetic voices of school children, their teachers and of speakers that were unfamiliar to them and used each of these voices to create variants of two audio games: a memory game and a labyrinth game. Results show that pupils had significantly longer engagement times and better performance when playing games that used synthetic voices built with their own voices. This result was observed even though the children reported not recognising the synthetic voice as their own after the experiment was over. These findings could be used to improve the design of audio games and lecture books for blind and visually impaired children.

We propose to use a perceptually-oriented domain to improve the quality of text-to-speech generated by deep neural networks (DNNs). We train a DNN that predicts the parameters required for speech reconstruction but whose cost function is calculated in another domain. In this paper, to represent this perceptual domain we extract an approximated version of the Spectro-Temporal Excitation Pattern that was originally proposed as part of a model of hearing speech in noise. We train DNNs that pre-dict band aperiodicity, fundamental frequency and Mel cepstral coefficients and compare generated speech when the spectral cost function is defined in the Mel cepstral, warped log spec-trum or perceptual domains. Objective results indicate that the perceptual domain system achieves the highest quality. Index Terms: speech synthesis, DNN, objective measures, spectral analysis

Tallying the numbers of listeners that took part in subjective evaluations of synthetic speech at Interspeech 2014 showed that in more than 60% of papers conclusions are based on listening tests with less than 20 listeners. Our analysis of Blizzard 2013 data shows that for a MOS test measuring naturalness a stable level of significance is only reached when more than 30 listeners are used. In this paper, we set out a list of guidelines, i.e., a checklist for carrying out meaningful subjective evaluations. We further illustrate the importance of sentence coverage and number of listeners by presenting changes to rank order and number of significant pairs by re-analysing data from the Blizzard Challenge 2013.

The generated speech of hidden Markov model (HMM)-based statistical parametric speech synthesis still sounds “muffled.” One cause of this degradation in speech quality may be the loss of fine spectral structures. In this paper, we propose to use a deep generative architecture, a deep neural network (DNN) generatively trained, as a postfilter. The network models the conditional probability of the spectrum of natural speech given that of synthetic speech to compensate for such gap between synthetic and natural speech. The proposed probabilistic postfilter is generatively trained by cascading two restricted Boltzmann machines (RBMs) or deep belief networks (DBNs) with one bidirectional associative memory (BAM). We devised two types of DNN postfilters: one operating in the mel-cepstral domain and the other in the higher dimensional spectral domain. We compare these two new data-driven postfilters with other types of postfilters that are currently used in speech synthesis: a fixed mel-cepstral based postfilter, the global variance based parameter generation, and the modulation spectrum-based enhancement. Subjective evaluations using the synthetic voices of a male and female speaker confirmed that the proposed DNN-based postfilter in the spectral domain significantly improved the segmental quality of synthetic speech compared to that with conventional methods.

In this paper we propose a deep neural network to model the conditional probability of the spectral differences between nat-ural and synthetic speech. This allows us to reconstruct the spectral fine structures in speech generated by HMMs. We com-pared the new stochastic data-driven postfilter with global vari-ance based parameter generation and modulation spectrum en-hancement. Our results confirm that the proposed method sig-nificantly improves the segmental quality of synthetic speech compared to the conventional methods. Index Terms: HMM, speech synthesis, DNN, modulation spectrum, postfilter, segmental quality

In this paper we analyse the effect of speech corpus and com-pression method on the intelligibility of synthesized speech at fast rates. We recorded English and German language voice tal-ents at a normal and a fast speaking rate and trained an HSMM-based synthesis system based on the normal and the fast data of each speaker. We compared three compression methods: scal-ing the variance of the state duration model, interpolating the duration models of the fast and the normal voices, and applying a linear compression method to generated speech. Word recog-nition results for the English voices show that generating speech at normal speaking rate and then applying linear compression resulted in the most intelligible speech at all tested rates. A similar result was found when evaluating the intelligibility of the natural speech corpus. For the German voices, interpolation was found to be better at moderate speaking rates but the linear method was again more successful at very high rates, for both blind and sighted participants. These results indicate that using fast speech data does not necessarily create more intelligible voices and that linear compression can more reliably provide higher intelligibility, particularly at higher rates. Index Terms: fast speech, HMM-based speech synthesis, blind users

In order to predict which words in a sentence are harder to un-derstand in noise it is necessary to consider not only audibility but also semantic or linguistic information. This paper focuses on using linguistic predictability to inform an intelligibility en-hancement method that uses Lombard-adapted synthetic speech to modify low predictable words in Speech Perception in Noise (SPIN) test sentences. Word intelligibility in the presence of speech-shaped noise was measured using plain, Lombard and a combination of the two synthetic voices. The findings show that the Lombard voice increases intelligibility in noise but the intelligibility gap between words in a high and low predictable context still remains. Using a Lombard voice when a word is unpredictable is a good strategy, but if a word is predictable from its context the Lombard benefit only occurs when other words in the sentence are also modified. Index Terms: intelligibility enhancement, speech in noise, HMM-based speech synthesis, SPIN test

In order to improve the intelligibility of HMM-generated Text-to-Speech (TTS) in noise, this work evaluates several speech enhancement methods, exploring combinations of noise-independent and - dependent approaches as well as algorithms previously developed for natural speech. We evaluate one noise-dependent method proposed for TTS, based on the glimpse proportion measure, and three approaches originally proposed for natural speech - one that estimates the noise and is based on the speech intelligibility index, and two noise-independent methods based on different spectral shaping techniques followed by dynamic range compression. We demonstrate how these methods influence the average spectra for different phone classes. We then present results of a listening experiment with speech-shaped noise and a competing speaker. A few methods made the TTS voice even more intelligible than the natural one. Although noise-dependent methods did not improve gains, the intelligibility differences found in distinct noises motivates such dependency.

Speech output is used extensively, including in situations where correct message reception is threatened by adverse listening conditions. Recently, there has been a growing interest in algorithmic modifications that aim to increase the intelligibility of both natural and synthetic speech when presented in noise. The Hurricane Challenge is the first large-scale open evaluation of algorithms designed to enhance speech intelligibility. Eighteen systems operating on a common data set were subjected to extensive listening tests and compared to unmodified natural and text-to-speech (TTS) baselines. The best-performing systems achieved gains over unmodified natural speech of 4.4 and 5.1 dB in competing speaker and stationary noise respectively, while TTS systems made gains of 5.6 and 5.1 dB over their baseline. Surprisingly, for most conditions the largest gains were observed for noise-independent algorithms, suggesting that performance in this task can be further improved by exploiting information in the masking signal. Index Terms: intelligibility, speech modification, TTS 1.

This paper presents our entry to a speech-in-noise intelligibility enhancement evaluation: the Hurricane Challenge. The system consists of a Text-To-Speech voice manipulated through a combination of enhancement strategies, each of which is known to be individually successful: a perceptually-motivated spectral shaper based on the Glimpse Proportion measure, dynamic range compression, and adaptation to Lombard excitation and duration patterns. We achieved substantial intelligibility improvements relative to unmodified synthetic speech: 4.9 dB in competing speaker and 4.1 dB in speech-shaped noise. An analysis conducted across this and other two similar evaluations shows that the spectral shaper and the compressor (both of which are loudness boosters) contribute most under higher SNR conditions, particularly for speech-shaped noise. Duration and excitation Lombard-adapted changes are more beneficial in lower SNR conditions, and for competing speaker noise.

In this paper we introduce a new cepstral coefficient extraction method based on an intelligibility measure for speech in noise, the Glimpse Proportion measure. This new method aims to increase the intelligibility of speech in noise by modifying the clean speech, and has applications in scenarios such as public announcement and car navigation systems. We first explain how the Glimpse Proportion measure operates and further show how we approximated it to integrate it into an existing spectral envelope parameter extraction method commonly used in the HMM-based speech synthesis framework. We then demonstrate how this new method changes the modelled spectrum according to the characteristics of the noise and show results for a listening test with vocoded and HMM-based synthetic speech. The test indicates that the proposed method can significantly improve intelligibility of synthetic speech in speech shaped noise.

We propose a method that modifies the Mel cepstral coefficients of HMM-generated synthetic speech in order to increase the intelligibility of the generated speech when heard by a listener in the presence of a known noise. This method is based on an approximation we previously proposed for the Glimpse Proportion measure. Here we show how to update the Mel cepstral coefficients using this measure as an optimization criterion and how to control the amount of distortion by limiting the frequency resolution of the modifications. To evaluate the method we built eight different voices from normal read-text speech data from a male speaker. Some voices were also built from Lombard speech data produced by the same speaker. Listening experiments with speech-shaped noise and with a single competing talker indicate that our method significantly improves intelligibility when compared to unmodified synthetic speech. The voices built from Lombard speech outperformed the proposed method particularly for the competing talker case. However, compared to a voice using only the spectral parameters from Lombard speech, the proposed method obtains similar or higher performance.

In this paper we evaluate four objective measures of speech with regards to intelligibility prediction of synthesized speech in diverse noisy situations. We evaluated three intelligibility measures, the Dau measure, the glimpse proportion and the Speech Intelligibility Index (SII) and a quality measure, the Perceptual Evaluation of Speech Quality (PESQ). For the generation of synthesized speech we used a state of the art HMM-based speech synthesis system. The noisy conditions comprised four additive noises. The measures were compared with subjective intelligibility scores obtained in listening tests. The results show the Dau and the glimpse measures to be the best predictors of intelligibility, with correlations of around 0.83 to subjective scores. All measures gave less accurate predictions of intelligibility for synthetic speech than have previously been found for natural speech; in particular the SII measure. In additional experiments, we processed the synthesized speech by an ideal binary mask before adding noise. The Glimpse measure gave the most accurate intelligibility predictions in this situation.

Synthetic speech can be modified to improve intelligibility in noise. In order to perform modifications automatically, it would be useful to have an objective measure that could predict the intelligibility of modified synthetic speech for human listeners. We analysed the impact on intelligibility – and on how well objective measures predict it – when we separately modify speaking rate, fundamental frequency, line spectral pairs and spectral peaks. Shifting LSPs can increase intelligibility for human listeners; other modifications had weaker effects. Among the objective measures we evaluated, the Dau model and the Glimpse proportion were the best predictors of human performance. Index Terms: objective measures for speech intelligibility, HMM-based speech synthesis, Lombard speech 1.