Recurrent neural network language models (RNNLMs) generally outperform n-gram language models when used in automatic speech recognition (ASR). Adapting RNNLMs to new domains is an open problem and current approaches can be categorised as either feature-based or model based. In feature-based adaptation, the input to the RNNLM is augmented with auxiliary features whilst model-based adaptation includes model fine-tuning and the introduction of adaptation layer(s) in the network. In this paper, the properties of both types of adaptation are investigated on multi-genre broadcast speech recognition. Existing techniques for both types of adaptation are reviewed and the proposed techniques for model-based adaptation, namely the linear hidden network adaptation layer and the K-component adaptive the RNNLM, are investigated. Moreover, new features derived from the acoustic domain are investigated for the RNNLM adaptation. The contributions of this paper include two hybrid adaptation techniques: the fine-tuning of feature-based RNNLMs and a feature-based adaptation layer. Moreover, the semi-supervised adaptation of RNNLMs using genre information is also proposed. The ASR systems were trained using 700 h of multi-genre broadcast speech. The gains obtained when using the RNNLM adaptation techniques proposed in this paper are consistent when using RNNLMs trained on an in-domain set of 10M words and on a combination of in-domain and out-of-domain sets of 660 M words, with approx. 10% perplexity and 2% relative word error rate improvements on a 28.3 h. test set. The best RNNLM adaptation techniques for ASR are also evaluated on a lightly supervised alignment of subtitles task for the same data, where the use of RNNLM adaptation leads to an absolute increase in the F-measure of 0.5%.

Abstract This paper describes a system for performing alignment of subtitles to audio on multigenre broadcasts using a lightly supervised approach. Accurate alignment of subtitles plays a substantial role in the daily work of media companies and currently still requires large human effort. Here, a comprehensive approach to performing this task in an automated way using lightly supervised alignment is proposed. The paper explores the different alternatives to speech segmentation, lightly supervised speech recognition and alignment of text streams. The proposed system uses lightly supervised decoding to improve the alignment accuracy by performing language model adaptation using the target subtitles. The system thus built achieves the third best reported result in the alignment of broadcast subtitles in the Multi–Genre Broadcast (MGB) challenge, with an F1 score of 88.8%. This system is available for research and other non–commercial purposes through webASR, the University of Sheffield’s cloud–based speech technology web service. Taking as inputs an audio file and untimed subtitles, webASR can produce timed subtitles in multiple formats, including TTML, WebVTT and SRT.

In earlier work we proposed a framework for speech source-filter separation that employs phase-based signal processing. This paper presents a further theoretical investigation of the model and optimisations that make the filter and source representations less sensitive to the effects of noise and better matched to downstream processing. To this end, first, in computing the Hilbert transform, the log function is replaced by the generalised logarithmic function. This introduces a tuning parameter that adjusts both the dynamic range and distribution of the phase-based representation. Second, when computing the group delay, a more robust estimate for the derivative is formed by applying a regression filter instead of using sample differences. The effectiveness of these modifications is evaluated in clean and noisy conditions by considering the accuracy of the fundamental frequency extracted from the estimated source, and the performance of speech recognition features extracted from the estimated filter. In particular, the proposed filter-based front-end reduces Aurora-2 WERs by 6.3% (average 0-20 dB) compared with previously reported results. Furthermore, when tested in a LVCSR task (Aurora-4) the new features resulted in 5.8% absolute WER reduction compared to MFCCs without performance loss in the clean/matched condition.

This paper proposes a framework for performing adaptation to complex and non-stationary background conditions in Automatic Speech Recognition (ASR) by means of asynchronous Constrained Maximum Likelihood Linear Regression (aCMLLR) transforms and asynchronous Noise Adaptive Training (aNAT). The proposed method aims to apply the feature transform that best compensates the background for every input frame. The implementation is done with a new Hidden Markov Model (HMM) topology that expands the usual left-to-right HMM into parallel branches adapted to different background conditions and permits transitions among them. Using this, the proposed adaptation does not require ground truth or previous knowledge about the background in each frame as it aims to maximise the overall log-likelihood of the decoded utterance. The proposed aCMLLR transforms can be further improved by retraining models in an aNAT fashion and by using speaker-based MLLR transforms in cascade for an efficient modelling of background effects and speaker. An initial evaluation in a modified version of the WSJCAM0 corpus incorporating 7 different background conditions provides a benchmark in which to evaluate the use of aCMLLR transforms. A relative reduction of 40.5% in Word Error Rate (WER) was achieved by the combined use of aCMLLR and MLLR in cascade. Finally, this selection of techniques was applied in the transcription of multi-genre media broadcasts, where the use of aNAT training, aCMLLR transforms and MLLR transforms provided a relative improvement of 2–3%.

Recurrent neural network language models (RNNLMs) have consistently outperformed n-gram language models when used in automatic speech recognition (ASR). This is because RNNLMs provide robust parameter estimation through the use of a continuous-space representation of words, and can generally model longer context dependencies than n-grams. The adaptation of RNNLMs to new domains remains an active research area and the two main approaches are: feature-based adaptation, where the input to the RNNLM is augmented with auxiliary features; and model-based adaptation, which includes model fine-tuning and introduction of adaptation layer(s) in the network. This paper explores the properties of both types of adaptation on multi-genre broadcast speech recognition. Two hybrid adaptation techniques are proposed, namely the finetuning of feature-based RNNLMs and the use of a feature-based adaptation layer. A method for the semi-supervised adaptation of RNNLMs, using topic model-based genre classification, is also presented and investigated. The gains obtained with RNNLM adaptation on a system trained on 700h. of speech are consistent using both RNNLMs trained on a small (10Mwords) and large set (660M words), with 10% perplexity and 2% word error rate improvements on a 28:3h. test set.

Huge amounts of digital videos are being produced and broadcast every day, leading to giant media archives. Effective techniques are needed to make such data accessible further. Automatic meta-data labelling of broadcast media is an essential task for multimedia indexing, where it is standard to use multi-modal input for such purposes. This paper describes a novel method for automatic detection of media genre and show identities using acoustic features, textual features or a combination thereof. Furthermore the inclusion of available meta-data, such as time of broadcast, is shown to lead to very high performance. Latent Dirichlet Allocation is used to model both acoustics and text, yielding fixed dimensional representations of media recordings that can then be used in Support Vector Machines based classification. Experiments are conducted on more than 1200 hours of TV broadcasts from the British Broadcasting Corporation (BBC), where the task is to categorise the broadcasts into 8 genres or 133 show identities. On a 200-hour test set, accuracies of 98.6% and 85.7% were achieved for genre and show identification respectively, using a combination of acoustic and textual features with meta-data.

This paper presents the most recent developments of the \nwebASR service (www.webasr.org), the world’s first web– \nbased fully functioning automatic speech recognition platform \nfor scientific use. Initially released in 2008, the functionalities \nof webASR have recently been expanded with 3 main goals in \nmind: Facilitate access through a RESTful architecture, that allows \nfor easy use through either the web interface or an API; allow \nthe use of input metadata when available by the user to improve \nsystem performance; and increase the coverage of available \nsystems beyond speech recognition. Several new systems \nfor transcription, diarisation, lightly supervised alignment and \ntranslation are currently available through webASR. The results \nin a series of well–known benchmarks (RT’09, IWSLT’12 and \nMGB’15 evaluations) show how these webASR systems provides \nstate–of–the–art performances across these tasks

This paper presents a range of error correction techniques aimed \nat improving the accuracy of a lightly supervised alignment task \nfor broadcast subtitles. Lightly supervised approaches are frequently \nused in the multimedia domain, either for subtitling \npurposes or for providing a more reliable source for training \nspeech–based systems. The proposed methods focus on directly \ncorrecting of the alignment output using different techniques to \ninfer word insertions and words with inaccurate time boundaries. \nThe features used by the classification models are the \noutputs from the alignment system, such as confidence measures, \nand word or segment duration. Experiments in this paper \nare based on broadcast material provided by the BBC to the \nMulti–Genre Broadcast (MGB) challenge participants. Results, \nshow that the order alignment F–measure improves up to 2.6% \nabsolute (15.8% relative) when combining insertion and word– \nboundary correction

This paper describes the Multi-Genre Broadcast (MGB) Challenge at ASRU 2015, an evaluation focused on speech recognition, speaker diarization, and "lightly supervised" alignment of BBC TV recordings. The challenge training data covered the whole range of seven weeks BBC TV output across four channels, resulting in about 1,600 hours of broadcast audio. In addition several hundred million words of BBC subtitle text was provided for language modelling. A novel aspect of the evaluation was the exploration of speech recognition and speaker diarization in a longitudinal setting — i.e. recognition of several episodes of the same show, and speaker diarization across these episodes, linking speakers. The longitudinal tasks also offered the opportunity for systems to make use of supplied metadata including show title, genre tag, and date/time of transmission. This paper describes the task data and evaluation process used in the MGB challenge, and summarises the results obtained.

This paper presents a new method for the discovery of latent domains in diverse speech data, for the use of adaptation of Deep Neural Networks (DNNs) for Automatic Speech Recognition. Our work focuses on transcription of multi-genre broadcast media, which is often only categorised broadly in terms of high level genres such as sports, news, documentary, etc. However, in terms of acoustic modelling these categories are coarse. Instead, it is expected that a mixture of latent domains can better represent the complex and diverse behaviours within a TV show, and therefore lead to better and more robust performance. We propose a new method, whereby these latent domains are discovered with Latent Dirichlet Allocation, in an unsupervised manner. These are used to adapt DNNs using the Unique Binary Code (UBIC) representation for the LDA domains. Experiments conducted on a set of BBC TV broadcasts, with more than 2,000 shows for training and 47 shows for testing, show that the use of LDA-UBIC DNNs reduces the error up to 13% relative compared to the baseline hybrid DNN models.

Speaker diarisation is the task of answering "who spoke when" within a multi-speaker audio recording. Diarisation of broadcast media typically operates on individual television shows, and is a particularly difficult task, due to a high number of speakers and challenging background conditions. Using prior knowledge, such as that from previous shows in a series, can improve performance. Longitudinal diarisation allows to use knowledge from previous audio files to improve performance, but requires finding matching speakers across consecutive files. This paper describes the University of Sheffield system for participation in the 2015 Multi-Genre Broadcast (MGB) challenge. The challenge required longitudinal diarisation of data from BBC archives, under very constrained resource settings. Our system consists of three main stages: speech activity detection using DNNs with novel adaptation and decoding methods; speaker segmentation and clustering, with adaptation of the DNN-based clustering models; and finally speaker linking to match speakers across shows. The final result on the development set of 19 shows from five different television series provided a Diarisation Error Rate of 50.77% in the diarisation and linking task.

We describe the University of Sheffield system for participation in the 2015 Multi-Genre Broadcast (MGB) challenge task of transcribing multi-genre broadcast shows. Transcription was one of four tasks proposed in the MGB challenge, with the aim of advancing the state of the art of automatic speech recognition, speaker diarisation and automatic alignment of subtitles for broadcast media. Four topics are investigated in this work: Data selection techniques for training with unreliable data, automatic speech segmentation of broadcast media shows, acoustic modelling and adaptation in highly variable environments, and language modelling of multi-genre shows. The final system operates in multiple passes, using an initial unadapted decoding stage to refine segmentation, followed by three adapted passes: a hybrid DNN pass with input features normalised by speaker-based cepstral normalisation, another hybrid stage with input features normalised by speaker feature-MLLR transformations, and finally a bottleneck-based tandem stage with noise and speaker factorisation. The combination of these three system outputs provides a final error rate of 27.5% on the official development set, consisting of 47 multi-genre shows.

Negative transfer in training of acoustic models for automatic speech recognition has been reported in several contexts such as domain change or speaker characteristics. This paper proposes a novel technique to overcome negative transfer by efficient selection of speech data for acoustic model training. Here data is chosen on relevance for a specific target. A submodular function based on likelihood ratios is used to determine how acoustically similar each training utterance is to a target test set. The approach is evaluated on a wide-domain data set, covering speech from radio and TV broadcasts, telephone conversations, meetings, lectures and read speech. Experiments demonstrate that the proposed technique both finds relevant data and limits negative transfer. Results on a 6--hour test set show a relative improvement of 4% with data selection over using all data in PLP based models, and 2% with DNN features.

Speech recognition systems are often highly domain dependent, a fact widely reported in the literature. However the concept of domain is complex and not bound to clear criteria. Hence it is often not evident if data should be considered to be out-of-domain. While both acoustic and language models can be domain specific, work in this paper concentrates on acoustic modelling. We present a novel method to perform unsupervised discovery of domains using Latent Dirichlet Allocation (LDA) modelling. Here a set of hidden domains is assumed to exist in the data, whereby each audio segment can be considered to be a weighted mixture of domain properties. The classification of audio segments into domains allows the creation of domain specific acoustic models for automatic speech recognition. Experiments are conducted on a dataset of diverse speech data covering speech from radio and TV broadcasts, telephone conversations, meetings, lectures and read speech, with a joint training set of 60 hours and a test set of 6 hours. Maximum A Posteriori (MAP) adaptation to LDA based domains was shown to yield relative Word Error Rate (WER) improvements of up to 16% relative, compared to pooled training, and up to 10%, compared with models adapted with human-labelled prior domain knowledge.

This paper presents a new approach for rapid adaptation in the presence of highly diverse scenarios that takes advantage of information describing the input signals. We introduce a new method for joint factorisation of the background and the speaker in an eigenspace MLLR framework: Joint Factor Eigenspace MLLR (JFEMLLR). We further propose to use contextual information describing the speaker and background, such as tags or more complex metadata, to provide an immediate estimation of the best MLLR transformation for the utterance. This provides instant adaptation, since it does not require any transcription from a previous decoding stage. Evaluation in a highly diverse Automatic Speech Recognition (ASR) task, a modified version of WSJCAM0, yields an improvement of 26.9% over the baseline, which is an extra 1.2% reduction over two-pass MLLR adaptation.

This paper presents a novel method for extracting acoustic features that characterise the background environment in audio recordings. These features are based on the output of an alignment that fits multiple parallel background-based Constrained Maximum Likelihood Linear Regression transformations asynchronously to the input audio signal. With this setup, the resulting features can track changes in the audio background like appearance and disappearance of music, applause or laughter, independently of the speakers in the foreground of the audio. The ability to provide this type of acoustic description in audiovisual data has many potential applications, including automatic classification of broadcast archives or improving automatic transcription and subtitling. In this paper, the performance of these features in a genre identification task in a set of 332 BBC shows is explored. The proposed background-tracking features outperform short-term Perceptual Linear Prediction features in this task using Gaussian Mixture Model classifiers (62% vs 72% accuracy). The use of more complex classifiers, Hidden Markov Models and Support Vector Machines, increases the performance of the system with the novel background-tracking features to 79% and 81% in accuracy respectively.

This paper presents a novel approach to separate the effects of speaker and background conditions by application of featuretransform based adaptation for Automatic Speech Recognition (ASR).So far factorisation has been shown to yield improvements in the case of utterance-synchronous environments.In this paper we show successful separation of conditions asynchronous with speech, such as background music.Our work takes account of the asynchronous nature of the background, by estimation of condition-specific Constrained Maximum Likelihood Linear Regression (CMLLR) transforms.In addition, speaker adaptation is performed, allowing to factorise speaker and background effects.Equally, background transforms are used asynchronously in the decoding process, using a modified Hidden Markov Model (HMM) topology which applies the optimal transform for each frame.Experimental results are presented on the WSJCAM0 corpus of British English speech, modified to contain controlled sections of background music.This addition of music degrades the baseline Word Error Rate (WER) from 10.1% to 26.4%.While synchronous factorisation with CMLLR transforms provides 28% relative improvement in WER over the baseline, our asynchronous approach increases this reduction to 33%.

In this paper, an unsupervised data-driven robust speech recognition approach is proposed based on a joint feature vector normalization and acoustic model adaptation. Feature vector normalization reduces the acoustic mismatch between training and testing conditions by mapping the feature vectors towards the training space. Model adaptation modifies the parameters of the acoustic models to match the test space. However, since neither is optimal, both approaches use an intermediate space between training and testing spaces to map either the feature vectors or acoustic models. The joint optimization of both approaches provides a common intermediate space with a better match between normalized feature vectors and adapted acoustic models. In this paper, feature vector normalization is based on a minimum mean square error (MMSE) criterion. A class dependent multi-environment model linear normalization (CD-MEMLIN) based on two classes (silence/speech) with a cross probability model (CD-MEMLIN-CPM) is used. CD-MEMLIN-CPM assumes that each class of clean and noisy spaces can be modeled with a Gaussian mixture model (GMM), training a linear transformation for each pair of Gaussians in an unsupervised data-driven training process. This feature vector normalization maps the recognition space feature vector to a normalized space. The acoustic model adaptation maps the training space to the normalized space by defining a set of linear transformations over an expanded HMM-state space, compensating for those degradations that the feature vector normalization is not able to model, like rotations. Experiments have been carried out with the Spanish SpeechDat Car database and Aurora 2 databases using both the standard Mel-frequency cepstral coefficient (MFCC) and advanced ETSI front-ends. Consistent improvements were reached for both corpora and front-ends. Using the standard MFCC front-end, a 92.08% average improvement on WER for Spanish SpeechDat Car and a 69.75% average improvement for clean condition evaluation of Aurora 2 was obtained, improving those results reached with ETSI advanced front-end (83.28% and 67.41%, respectively). Using the ETSI advanced front-end with the proposed solution, a 75.47% average improvement was obtained for the clean condition evaluation of Aurora 2 database.

Techniques are presented for detecting phoneme level mispronunciations in utterances obtained from a population of impaired children speakers. The intended application of these approaches is to use the resulting confidence measures to provide feedback to patients concerning the quality of pronunciations in utterances arising within interactive speech therapy sessions. The pronunciation verification scenario involves presenting utterances of known words to a phonetic decoder and generating confusion networks from the resulting phone lattices. Confidence measures are derived from the posterior probabilities obtained from the confusion networks. Phoneme level mispronunciation detection performance was significantly improved with respect to a baseline system by optimizing acoustic models and pronunciation models in the phonetic decoder and applying a nonlinear mapping to the confusion network posteriors.

This paper presents an approach to provide of lexical adaptation in Automatic Speech Recognition (ASR) of the disordered speech from a group of young impaired speakers. The outcome of an Acoustic Phonetic Decoder (APD) is used to learn new lexical variants of the 57-word vocabulary and add them to a lexicon personalized to each user. The possibilities of combination of this lexical adaptation with acoustic adaptation achieved through traditional Maximum A Posteriori (MAP) approaches are furtherer explored, and the results show the importance of matching the lexicon in the ASR decoding phase to the lexicon used for the acoustic adaptation.

This paper addresses the problem that disabled people face when accessing the new systems and technologies that are available nowadays. The use of speech technologies, specially helpful for motor handicapped people, becomes unapproachable when these people also suffer speech impairments, making the gap in the society wider for them. As a way to include speech impaired people in the technological society of today, two lines of work have been carried out. On one hand, a computer-aided speech therapy software has been developed for the speech training of children with different disabilities. This tool, available for free distribution, makes use of different state-of-the-art speech technologies to train different levels of the language. As a result of this work, the software is being used currently in several centers for special education with a very encouraging feedback about the capabilities of the system. On the other hand, research on the use of automatic speech recognition (ASR) systems for the speech impaired has been carried out. This work has focused on current techniques of speaker adaptation to know how these techniques, fruitfully used in other tasks, can deal with this specific kind of speech. The use of Maximum A Posterior (MAP) obtains an improvement of 60.61% compared to the results of a baseline speaker independent model.

We propose on-line unsupervised compensation technique for robust speech recognition that combines standard and throat microphone feature vectors. The solution, called Multi-Environment Model-based LInear Normalization with Throat microphone information, MEMLINT, is an extension of MEM-LIN formulation. Hence, standard microphone noisy space and throat microphone space are modelled as GMMs and a set of linear transformations are learnt from data associated to each pair of Gaussians (one for each GMM) using training stereo data. On the other hand, to compensate some kinds of degra-dation which are not considered in MEMLINT, we propose to use jointly an on-line unsupervised acoustic model adaptation method based on rotation transformations over an expanded HMM-state space (augMented stAte space acousTic dEcoder, MATE). Some experiments with an own recorded database were carried out, showing that the proposed approach significantly outperforms the single microphone approach. Index Terms: Throat microphone, robust speech recognition, feature vector normalization.

This paper presents a study of confidence measure based techniques for detecting phoneme level mispronunciations in utterances from impaired children with neuromuscular disorders. Several different adaptation scenarios are investigated to determine the effects of mismatched speaker characteristics and mismatched task domain on the ability to verify the phoneme level pronunciations. These techniques are evaluated in the context of a speech corpus where utterances were elicited from children in interactive speech therapy sessions involving a multimodal game-like environment. Results are presented in terms of phone detection charateristics where, for example, equal error rates of as low as 16.2 % were obtained for detecting instances where phonemes were deleted by impaired speakers. 1.

The new model reduces the impact of local spectral and temporal variability by estimating a finite set of spectral and temporal warping factors which are applied to speech at the frame level. Optimum warping factors are obtained while decoding in a locally constrained search. The model involves augmenting the states of a standard hidden Markov model (HMM), providing an additional degree of freedom. It is argued in this paper that this represents an efficient and effective method for compensating local variability in speech which may have potential application to a broader array of speech transformations. The technique is presented in the context of existing methods for frequency warping-based speaker normalization for ASR. The new model is evaluated in clean and noisy task domains using subsets of the Aurora 2, the Spanish Speech-Dat-Car, and the TIDIGITS corpora. In addition, some experiments are performed on a Spanish language corpus collected from a population of speakers with a range of speech disorders. It has been found that, under clean or not severely degraded conditions, the new model provides improvements over the standard HMM baseline. It is argued that the framework of local warping is an effective general approach to providing more flexible models of speaker variability.

An on-line unsupervised hybrid compensation technique is proposed to reduce the mismatch between training and testing conditions. It combines multi-environment model based linear normalization with cross-probability model based on GMMs (MEMLIN CPM) with a novel acoustic model adaptation method based on rotation transformations. Hence, a set of rotation transformations is estimated with clean and MEMLIN CPM-normalized training data by linear regression in an unsupervised process. Thus, in testing, each MEMLIN CPM normalized frame is decoded using a modified Viterbi algorithm and expanded acoustic models, which are obtained from the reference ones and the set of rotation transformations. To test the proposed solution, some experiments with Spanish SpeechDat Car database were carried out. MEMLIN CPM over standard ETSI front-end parameters reaches 83.89% of average improvement in WER, while the introduced hybrid solution goes up to 92.07%. Also, the proposed hybrid technique was tested with Aurora 2 database, obtaining an average improvement of 68.88% with clean training.

This paper describes the performance of the combination of Multi-Environment Model-based LInear Normalization, MEMLIN, which provides an estimation of the uncorrupted feature vector, with Maximum Likelihood Linear Regression, MLLR, for the collected database under the auspices of the IST-EU STREP project HIWIRE. In this work the results for the nonnative adaptation task (NNA) are presented. The HIWIRE project database consist on command and control aeronautics application utterances pronounced by non-native speakers which are digitally corrupted with airplane cockpit noise. Thus, three noise conditions are defined: low, medium and high noise. In the proposed system, each MEMLIN-normalized feature vector is decoded using the MLLR-adapted acoustic models. The experiments show that an important improvement is reached combining MEMLIN and MLLR methods for all kinds of non-native speakers and noise conditions. Index Terms: Hiwire project, robust speech recognition, nonnative adaptation task.

In this paper, a set of feature vector normalization methods based on the minimum mean square error (MMSE) criterion and stereo data is presented. They include multi-environment model-based linear normalization (MEMLIN), polynomial MEMLIN (P-MEMLIN), multi-environment model-based histogram normalization (MEMHIN), and phoneme-dependent MEMLIN (PD-MEMLIN). Those methods model clean and noisy feature vector spaces using Gaussian mixture models (GMMs). The objective of the methods is to learn a transformation between clean and noisy feature vectors associated with each pair of clean and noisy model Gaussians. The direct approach to learn the transformation is by using stereo data; that is, noisy feature vectors and the corresponding clean feature vectors. In this paper, however, a nonstereo data based training procedure, is presented. The transformations can be modeled just like a bias vector (MEMLIN), or by using a first-order polynomial (P-MEMLIN) or a nonlinear function based on histogram equalization (MEMHIN). Further improvements are obtained by using phoneme-dependent bias vector transformation (PD-MEMLIN). In PD-MEMLIN, the clean and noisy feature vector spaces are split into several phonemes, and each of them is modeled as a GMM. Those methods achieve significant word error rate improvements over others that are based on similar targets. The experimental results using the SpeechDat Car database show an average improvement in word error rate greater than 68% in all cases compared to the baseline when using the original clean acoustic models, and up to 83% when training acoustic models on the new normalized feature space

This paper presents a novel acoustic modeling framework that naturally extends the Hidden Markov Model (HMM) approach. The novel models reduce the errors caused by speaker variability by means of a local spectral mismatch reduction. A more complex and flexible speech production scheme can be assumed, in which the local temporal and frequency elastic deformations of the speech are captured by the model. In the new framework the states of a standard HMM, which are usually associated with temporal transitions, are expanded so that a new degree of freedom for the model is provided and it is then possible to estimate an optimum frequency warping factor at the same time as the decoder finds the best state sequence. In the local spectral warping based models the states become time-frequency related states and the number of parameters of the model is comparable to the standard HMM since they share a certain amount of parameters as it will be shown. The novel models are evaluated in the noise-free TIDIGITS corpus, which includes connected digits uttered by male, female and children. It has been found that, under speaker group (age-gender) mismatch conditions, the local frequency warping reduced Word Error Rate (WER) in mean by a 70%, using the initial models. When matched speaker group conditions were tested the error was reduced in mean in a 9.7% after reestimating the models. Index Terms: speaker variability, local frequency warping.

In this work, we study the variations in the time and frequency domains inside a Spanish language corpus of speakers with nonpathological and pathological speech. We show how pathological speech has a greater variability in the duration of the words than non-pathological speech, while in the frequency domain we show that the vowels confusability increases by a 18%. The baseline experiments in Automatic Speech Recognition (ASR) with this corpus demonstrate that this variability causes a loss in the performance of ASR systems. To reduce the impact of time and frequency variability we use a recent Vocal Tract Length Normalization (VTLN) system: MATE (augMented stAte space acousTic modEl), as a way of improving the performance of ASR systems when dealing with speakers who suffer any kind of speech pathology. Experiments with MATE show a 17.04 % and 11.19 % WER reduction by using frequency and time MATE respectively. 1.