Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Hidden Markov models and neural networks for speech recognition

The Hidden Markov Model (HMMs) is one of the most successful modeling approaches for acoustic events in speech recognition, and more recently it has proven useful for several problems in biological sequence analysis. Although the HMM is good at capturing the temporal nature of processes such as speech, it has a very limited capacity for recognizing complex patterns involving more than first order dependencies in the observed data sequences. This is due to the first order state process and the assumption of state conditional independence between observations. Artificial Neural Networks (NNs) are almost the opposite: they cannot model dynamic, temporally extended phenomena very well, but are good at static classification and regression tasks. Combining the two frameworks in a sensible way can therefore lead to a more powerful model with better classification abilities. The overall aim of this work has been to develop a probabilistic hybrid of hidden Markov models and neural networks and ..

Automatic Speech Recognition for Documenting Endangered First Nations Languages

Automatic speech recognition (ASR) for low-resource languages is an active field of research. Over the past years with the advent of deep learning, impressive achievements have been reported using minimal resources. As many of the world’s languages are getting extinct every year, with every dying language we lose intellect, culture, values, and tradition which generally pass down for long generations. Linguists throughout the world have already initiated many projects on language documentation to preserve such endangered languages. Automatic speech recognition is a solution to accelerate the documentation process reducing the annotation time for field linguists as well as the overall cost of the project. A traditional speech recognizer is trained on thousands of hours of acoustic data and a phonetic dictionary that includes all words from the language. End-to-End ASR systems have shown dramatic improvement for major languages. Especially, recent advancement in self-supervised representation learning which takes advantage of large corpora of untranscribed speech data has become the state-of-the-art for speech recognition technology. However, for resource-constrained languages, the technology is not tested in depth. In this thesis, we explore both traditional methods of ASR and state-of-the-art end-to-end systems for modeling a critically endangered Athabascan language known as Upper Tanana. In our first approach, we investigate traditional models with a comparative study on feature selection and a performance comparison with deep hybrid models. With limited resources at our disposal, we build a working ASR system based on a grapheme-to-phoneme (G2P) phonetic dictionary. The acoustic model can also be used as a separate forced alignment tool for the automatic alignment of training data. The results show that the GMM-HMM methods outperform deep hybrid models in low-resource acoustic modeling. In our second approach, we propose using Domain-adapted Cross-lingual Speech Recognition (DA-XLSR) for an ASR system, developed over the wav2vec 2.0 framework that utilizes pretrained transformer models leveraging cross lingual data for building an acoustic representation. The proposed system uses a multistage transfer learning process in order to fine tune the final model. To supplement the limited data, we compile a data augmentation strategy combining six augmentation techniques. The speech model uses Connectionist Temporal Classification (CTC) for an alignment free training and does not require any pronunciation dictionary or language model. Experiments from the second approach demonstrate that it can outperform the best traditional or end-to-end models in terms of word error rate (WER) and produce a powerful utterance level transcription. On top of that, the augmentation strategy is tested on several end-to-end models, and it provides a consistent improvement in performance. While the best proposed model can currently reduce the WER significantly, it may still require further research to completely replace the need for human transcribers

Large vocabulary continuous speech recognition using linguistic features and constraints

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 111-123).Automatic speech recognition (ASR) is a process of applying constraints, as encoded in the computer system (the recognizer), to the speech signal until ambiguity is satisfactorily resolved to the extent that only one sequence of words is hypothesized. Such constraints fall naturally into two categories. One deals with the ordering of words (syntax) and organization of their meanings (semantics, pragmatics, etc). The other governs how speech signals are related to words, a process often termed as lexical access". This thesis studies the Huttenlocher-Zue lexical access model, its implementation in a modern probabilistic speech recognition framework and its application to continuous speech from an open vocabulary. The Huttenlocher-Zue model advocates a two-pass lexical access paradigm. In the first pass, the lexicon is effectively pruned using broad linguistic constraints. In the original Huttenlocher-Zue model, the authors had proposed six linguistic features motivated by the manner of pronunciation. The first pass classifies speech signals into a sequence of linguistic features, and only words that match this sequence - the cohort - are activated. The second pass performs a detailed acoustic phonetic analysis within the cohort to decide the identity of the word. This model differs from the lexical access model nowadays commonly employed in speech recognizers where detailed acoustic phonetic analysis is performed directly and lexical items are retrieved in one pass. The thesis first studies the implementation issues of the Huttenlocher-Zue model. A number of extensions to the original proposal are made to take advantage of the existing facilities of a probabilistic, graph-based recognition framework and, more importantly, to model the broad linguistic features in a data-driven approach. First, we analyze speech signals along the two diagonal dimensions of manner and place of articulation, rather than the manner dimension alone. Secondly, we adopt a set of feature-based landmarks optimized for data-driven modeling as the basic recognition units, and Gaussian mixture models are trained for these units. We explore information fusion techniques to integrate constraints from both the manner and place dimensions, as well as examining how to integrate constraints from the feature-based first pass with the second pass of detailed acoustic phonetic analysis. Our experiments on a large-vocabulary isolated word recognition task show that, while constraints from each individual feature dimension provide only limited help in this lexical access model, the utilization of both dimensions and information fusion techniques leads to significant performance gain over a one-pass phonetic system. The thesis then proposes to generalize the original Huttenlocher-Zue model, which limits itself to only isolated word tasks, to handle continuous speech. With continuous speech, the search space for both stages is infinite if all possible word sequences are allowed. We generalize the original cohort idea from the Huttenlocher-Zue proposal and use the bag of words of the N-best list of the first pass as cohorts for continuous speech. This approach transfers the constraints of broad linguistic features into a much reduced search space for the second stage. The thesis also studies how to recover from errors made by the first pass, which is not discussed in the original Huttenlocher- Zue proposal. In continuous speech recognition, a way of recovering from errors made in the first pass is vital to the performance of the over-all system. We find empirical evidence that such errors tend to occur around function words, possibly due to the lack of prominence, in meaning and henceforth in linguistic features, of such words. This thesis proposes an error-recovery mechanism based on empirical analysis on a development set for the two-pass lexical access model. Our experiments on a medium- sized, telephone-quality continuous speech recognition task achieve higher accuracy than a state-of-the-art one-pass baseline system. The thesis applies the generalized two-pass lexical access model to the challenge of recognizing continuous speech from an open vocabulary. Telephony information query systems often need to deal with a large list of words that are not observed in the training data, for example the city names in a weather information query system. The large portion of vocabulary unseen in the training data - the open vocabulary - poses a serious data-sparseness problem to both acoustic and language modeling. A two-pass lexical access model provides a solution by activating a small cohort within the open vocabulary in the first pass, thus significantly reducing the data- sparseness problem. Also, the broad linguistic constraints in the first pass generalize better to unseen data compared to finer, context-dependent acoustic phonetic models. This thesis also studies a data-driven analysis of acoustic similarities among open vocabulary items. The results are used for recovering possible errors in the first pass. This approach demonstrates an advantage over a two-pass approach based on specific semantic constraints. In summary, this thesis implements the original Huttenlocher-Zue two-pass lexical access model in a modern probabilistic speech recognition framework. This thesis also extends the original model to recognize continuous speech from an open vocabulary, with our two-stage model achieving a better performance than the baseline system. In the future, sub-lexical linguistic hierarchy constraints, such as syllables, can be introduced into this two-pass model to further improve the lexical access performance.by Min Tang.Ph.D