For a low-resourced language like Swiss German with no standard orthography and a significant variation in its written form, spoken language resources are more likely to come with translations than transcriptions. Moreover, the desired output of an automatic transcription system for Swiss German multi-dialectal speech is Standard German. This, in turn, is due to many applications that include our TV Box voice assistant and broadcast media. It follows that a translation is usually required as Swiss German and Standard German have mismatches on all linguistic levels. Unfortunately, there are not enough parallel text corpora available for training a proper translation system, nor enough in-domain speech translation (ST) data for training an ST system. We aim at investigating an end-to-end approach for multi-dialect Swiss German ST using transfer learning. Our ST model is based on an encoder-decoder architecture where we initialize the encoder with a cross-lingual speech representation model which is adapted to in-domain Swiss German speech data. We demonstrate that training the decoder on an out-of-domain ST corpus by preserving the encoder unit and then fine-tuning on in-domain ST data can be more effective than a cascade or vanilla direct ST.

We introduce a dictionary containing normalized forms of common words in various Swiss German dialects into High German. As Swiss German is, for now, a predominantly spoken language, there is a significant variation in the written forms, even between speakers of the same dialect. To alleviate the uncertainty associated with this diversity, we complement the pairs of Swiss German - High German words with the Swiss German phonetic transcriptions (SAMPA). This dictionary becomes thus the first resource to combine large-scale spontaneous translation with phonetic transcriptions. Moreover, we control for the regional distribution and insure the equal representation of the major Swiss dialects. The coupling of the phonetic and written Swiss German forms is powerful. We show that they are sufficient to train a Transformer-based phoneme to grapheme model that generates credible novel Swiss German writings. In addition, we show that the inverse mapping - from graphemes to phonemes - can be modeled with a transformer trained with the novel dictionary. This generation of pronunciations for previously unknown words is key in training extensible automated speech recognition (ASR) systems, which are key beneficiaries of this dictionary.

In sequence modeling tasks the token order matters, but this information can be partially lost due to the discretization of the sequence into data points. In this paper, we study the imbalance between the way certain token pairs are included in data points and others are not. We denote this a token order imbalance (TOI) and we link the partial sequence information loss to a diminished performance of the system as a whole, both in text and speech processing tasks. We then provide a mechanism to leverage the full token order information—Alleviated TOI—by iteratively overlapping the token composition of data points. For recurrent networks, we use prime numbers for the batch size to avoid redundancies when building batches from overlapped data points. The proposed method achieved state of the art performance in both text and speech related tasks.

Speech Emotion Recognition (SER) has been shown to benefit from many of the recent advances in deep learning, including recurrent based and attention based neural network architectures as well. Nevertheless, performance still falls short of that of humans. In this work, we investigate whether SER could benefit from the self-attention and global windowing of the transformer model. We show on the IEMOCAP database that this is indeed the case. Finally, we investigate whether using the distribution of, possibly conflicting, annotations in the training data, as soft targets could outperform a majority voting. We prove that this performance increases with the agreement level of the annotators.

In this work, we study the use of attention mechanisms to enhance the performance of the state-of-the-art deep learning model in Speech Emotion Recognition (SER). We introduce a new Long Short-Term Memory (LSTM)-based neural network attention model which is able to take into account the temporal information in speech during the computation of the attention vector. The proposed LSTM-based model is evaluated on the IEMOCAP dataset using a 5-fold cross-validation scheme and achieved 68.8% weighted accuracy on 4 classes, which outperforms the state-of-the-art models.

Using phonological speech vocoding, we propose a platform for exploring relations between phonology and speech processing, and in broader terms, for exploring relations between the abstract and physical structures of a speech signal. Our goal is to make a step towards bridging phonology and speech processing and to contribute to the program of Laboratory Phonology. We show three application examples for laboratory phonology: compositional phonological speech modelling, a comparison of phonological systems and an experimental phonological parametric text-to-speech (TTS) system. The featural representations of the following three phonological systems are considered in this work: (i) Government Phonology (GP), (ii) the Sound Pattern of English (SPE), and (iii) the extended SPE (eSPE). Comparing GP- and eSPE-based vocoded speech, we conclude that the latter achieves slightly better results than the former. However, GP – the most compact phonological speech representation – performs comparably to the systems with a higher number of phonological features. The parametric TTS based on phonological speech representation, and trained from an unlabelled audiobook in an unsupervised manner, achieves intelligibility of 85% of the state-of-the-art parametric speech synthesis. We envision that the presented approach paves the way for researchers in both fields to form meaningful hypotheses that are explicitly testable using the concepts developed and exemplified in this paper. On the one hand, laboratory phonologists might test the applied concepts of their theoretical models, and on the other hand, the speech processing community may utilize the concepts developed for the theoretical phonological models for improvements of the current state-of-the-art applications.

We describe here a collection of speech data of bilingual and trilingual speakers of English, French, German and Italian. In the context of speech to speech translation (S2ST), this database is designed for several purposes and studies: training CLSA systems (cross-language speaker adaptation), conveying emphasis through S2ST systems, and evaluating TTS systems. More precisely, 36 speakers judged as accentless (22 bilingual and 14 trilingual speakers) were recorded for a set of 171 prompts in two or three languages, amounting to a total of 24 hours of speech. These sets of prompts include 100 sentences from news, 25 sentences from Europarl, the same 25 sentences with one acted emphasised word, 20 semantically unpredictable sentences, and finally a 240-word long text. All in all, it yielded 64 bilingual session pairs of the six possible combinations of the four languages. The database is freely available for non-commercial use and scientific research purposes.

Abstract Amplitude demodulation (AM) is a signal decomposition technique by which a signal can be decomposed to a product of two signals, i.e, a quickly varying carrier and a slowly varying modulator. In this work, the probabilistic amplitude demodulation (PAD) features are used to improve prosody in speech synthesis. The PAD is applied iteratively for generating syllable and stress amplitude modulations in a cascade manner. The PAD features are used as a secondary input scheme along with the standard text-based input features in statistical parametric speech syn- thesis. Specifically, deep neural network (DNN)-based speech synthesis is used to evaluate the importance of these features. Objective evaluation has shown that the proposed system using the PAD features has improved mainly prosody modelling; it outperforms the baseline system by approximately 5% in terms of relative reduction in root mean square error (RMSE) of the fundamental frequency (F0). The significance of this improvement is validated by subjective evaluation of the overall speech quality, achieving 38.6% over 19.5% preference score in respect to the baseline system, in an ABX test.

Most current very low bit rate (VLBR) speech coding systems use hidden Markov model (HMM) based speech recognition and synthesis techniques. This allows transmission of information (such as phonemes) segment by segment; this decreases the bit rate. However, an encoder based on a phoneme speech recognition may create bursts of segmental errors; these would be further propagated to any suprasegmental (such as syllable) information coding. Together with the errors of voicing detection in pitch parametrization, HMM-based speech coding leads to speech discontinuities and unnatural speech sound artifacts. In this paper, we propose a novel VLBR speech coding framework based on neural networks (NNs) for end-to-end speech analysis and synthesis without HMMs. The speech coding framework relies on a phonological (subphonetic) representation of speech. It is designed as a composition of deep and spiking NNs: a bank of phonological analyzers at the transmitter, and a phonological synthesizer at the receiver. These are both realized as deep NNs, along with a spiking NN as an incremental and robust encoder of syllable boundaries for coding of continuous fundamental frequency (F0). A combination of phonological features defines much more sound patterns than phonetic features defined by HMM-based speech coders; this finer analysis/synthesis code contributes to smoother encoded speech. Listeners significantly prefer the NN-based approach due to fewer discontinuities and speech artifacts of the encoded speech. A single forward pass is required during the speech encoding and decoding. The proposed VLBR speech coding operates at a bit rate of approximately 360 bits/s.

Current very low bit rate speech coders are, due to complexity limitations, designed to work off-line. This paper investigates incremental speech coding that operates real-time and incrementally (i.e., encoded speech depends only on already-uttered speech without the need of future speech information). Since human speech communication is asynchronous (i.e., different information flows being simultaneously processed), we hypothesized that such an incremental speech coder should also operate asynchronously. To accomplish this task, we describe speech coding that reflects the human cortical temporal sampling that packages information into units of different temporal granularity, such as phonemes and syllables, in parallel. More specifically, a phonetic vocoder-cascaded speech recognition and synthesis systems-extended with syllable-based information transmission mechanisms is investigated. There are two main aspects evaluated in this work, the synchronous and asynchronous coding. Synchronous coding refers to the case when the phonetic vocoder and speech generation process depend on the syllable boundaries during encoding and decoding respectively. On the other hand, asynchronous coding refers to the case when the phonetic encoding and speech generation processes are done independently of the syllable boundaries. Our experiments confirmed that the asynchronous incremental speech coding performs better, in terms of intelligibility and overall speech quality, mainly due to better alignment of the segmental and prosodic information. The proposed vocoding operates at an uncompressed bit rate of 213 bits/sec and achieves an average communication delay of 243 ms.

LIDIAP

A fusion scheme of phone duration models (PDMs) is presented in this work. Specifically, a support vector regression (SVR)-fusion model is fed with the predictions of a group of independent PDMs operating in parallel. The American-English KED TIMIT and the Greek WCL-1 databases are used for evaluating the PDMs and the fusion scheme. The fusion scheme contributes to the accuracy improvement over the best individual model, achieving a relative reduction of the mean absolute error (MAE) and the root mean square error (RMSE), by 1.9% and 2.0% on KED TLVHT, and 2.6% and 1.8% respectively on WCL-1. Moreover, for evaluating the impact the accuracy improvement will have on synthetic speech, perceptual evaluation test was performed. This test showed that the accuracy improvement achieved by the SVR-fusion would contribute to the improvement of the naturalness of synthetic speech.

A large Greek-English Dictionary with 81,515 entries, 192,592 translations into English and 50,106 usage examples with their translation has been developed in combined printed and electronic (DVD) form. The electronic dictionary features unique facilities for searching the entire or any part of the Greek and English section, and has incorporated a series of speech and language processing tools which efficiently assist learners of Greek and English. This paper presents the human-machine interface of the dictionary and the most important tools, i.e. the TTS-synthesizers for Greek and English, the lemmatizers for Greek and English, the Grapheme-to-Phoneme converter for Greek and the syllabification system for Greek.

In this paper we cope with the task of modeling phoneme duration for Greek speech synthesis. In particular we apply well established machine learning approaches to the WCL-1 prosodic database for predicting segmental durations from shallow morphosyntactic and prosodic features. We employ decision trees, instance based learning and linear regression. Trained on a 5500 word database, both CART and linear regression models proved to be the most effective in terms for the task with a root mean square error off 0. 0252 and 0.0251 respectively.