Towards an automatic speech recognition system for use by deaf students in lectures

According to the Royal National Institute for Deaf people there are nearly 7.5 million hearing-impaired people in Great Britain. Human-operated machine transcription systems, such as Palantype, achieve low word error rates in real-time. The disadvantage is that they are very expensive to use because of the difficulty in training operators, making them impractical for everyday use in higher education. Existing automatic speech recognition systems also achieve low word error rates, the disadvantages being that they work for read speech in a restricted domain. Moving a system to a new domain requires a large amount of relevant data, for training acoustic and language models. The adopted solution makes use of an existing continuous speech phoneme recognition system as a front-end to a word recognition sub-system. The subsystem generates a lattice of word hypotheses using dynamic programming with robust parameter estimation obtained using evolutionary programming. Sentence hypotheses are obtained by parsing the word lattice using a beam search and contributing knowledge consisting of anti-grammar rules, that check the syntactic incorrectness’ of word sequences, and word frequency information. On an unseen spontaneous lecture taken from the Lund Corpus and using a dictionary containing "2637 words, the system achieved 815% words correct with 15% simulated phoneme error, and 73.1% words correct with 25% simulated phoneme error. The system was also evaluated on 113 Wall Street Journal sentences. The achievements of the work are a domain independent method, using the anti- grammar, to reduce the word lattice search space whilst allowing normal spontaneous English to be spoken; a system designed to allow integration with new sources of knowledge, such as semantics or prosody, providing a test-bench for determining the impact of different knowledge upon word lattice parsing without the need for the underlying speech recognition hardware; the robustness of the word lattice generation using parameters that withstand changes in vocabulary and domain

Modeling Dependencies in Natural Languages with Latent Variables

In this thesis, we investigate the use of latent variables to model complex dependencies in natural languages. Traditional models, which have a fixed parameterization, often make strong independence assumptions that lead to poor performance. This problem is often addressed by incorporating additional dependencies into the model (e.g., using higher order N-grams for language modeling). These added dependencies can increase data sparsity and/or require expert knowledge, together with trial and error, in order to identify and incorporate the most important dependencies (as in lexicalized parsing models). Traditional models, when developed for a particular genre, domain, or language, are also often difficult to adapt to another. In contrast, previous work has shown that latent variable models, which automatically learn dependencies in a data-driven way, are able to flexibly adjust the number of parameters based on the type and the amount of training data available. We have created several different types of latent variable models for a diverse set of natural language processing applications, including novel models for part-of-speech tagging, language modeling, and machine translation, and an improved model for parsing. These models perform significantly better than traditional models. We have also created and evaluated three different methods for improving the performance of latent variable models. While these methods can be applied to any of our applications, we focus our experiments on parsing. The first method involves self-training, i.e., we train models using a combination of gold standard training data and a large amount of automatically labeled training data. We conclude from a series of experiments that the latent variable models benefit much more from self-training than conventional models, apparently due to their flexibility to adjust their model parameterization to learn more accurate models from the additional automatically labeled training data. The second method takes advantage of the variability among latent variable models to combine multiple models for enhanced performance. We investigate several different training protocols to combine self-training with model combination. We conclude that these two techniques are complementary to each other and can be effectively combined to train very high quality parsing models. The third method replaces the generative multinomial lexical model of latent variable grammars with a feature-rich log-linear lexical model to provide a principled solution to address data sparsity, handle out-of-vocabulary words, and exploit overlapping features during model induction. We conclude from experiments that the resulting grammars are able to effectively parse three different languages. This work contributes to natural language processing by creating flexible and effective latent variable models for several different languages. Our investigation of self-training, model combination, and log-linear models also provides insights into the effective application of these machine learning techniques to other disciplines

Incorporating Punctuation Into the Sentence Grammar: A Lexicalized Tree Adjoining Grammar Perspective

Punctuation helps us to structure, and thus to understand, texts. Many uses of punctuation straddle the line between syntax and discourse, because they serve to combine multiple propositions within a single orthographic sentence. They allow us to insert discourse-level relations at the level of a single sentence. Just as people make use of information from punctuation in processing what they read, computers can use information from punctuation in processing texts automatically. Most current natural language processing systems fail to take punctuation into account at all, losing a valuable source of information about the text. Those which do mostly do so in a superficial way, again failing to fully exploit the information conveyed by punctuation. To be able to make use of such information in a computational system, we must first characterize its uses and find a suitable representation for encoding them. The work here focuses on extending a syntactic grammar to handle phenomena occurring within a single sentence which have punctuation as an integral component. Punctuation marks are treated as full-fledged lexical items in a Lexicalized Tree Adjoining Grammar, which is an extremely well-suited formalism for encoding punctuation in the sentence grammar. Each mark anchors its own elementary trees and imposes constraints on the surrounding lexical items. I have analyzed data representing a wide variety of constructions, and added treatments of them to the large English grammar which is part of the XTAG system. The advantages of using LTAG are that its elementary units are structured trees of a suitable size for stating the constraints we are interested in, and the derivation histories it produces contain information the discourse grammar will need about which elementary units have used and how they have been combined. I also consider in detail a few particularly interesting constructions where the sentence and discourse grammars meet-appositives, reported speech and uses of parentheses. My results confirm that punctuation can be used in analyzing sentences to increase the coverage of the grammar, reduce the ambiguity of certain word sequences and facilitate discourse-level processing of the texts

Identification and correction of speech repairs in the context of an automatic speech recognition system

Recent advances in automatic speech recognition systems for read (dictated) speech have led researchers to confront the problem of recognising more spontaneous speech. A number of problems, such as disfluencies, appear when read speech is replaced with spontaneous speech. In this work we deal specifically with what we class as speech-repairs. Most disfluency processes deal with speech-repairs at the sentence level. This is too late in the process of speech understanding. Speech recognition systems have problems recognising speech containing speech-repairs. The approach taken in this work is to deal with speech-repairs during the recognition process. Through an analysis of spontaneous speech the grammatical structure of speech- repairs was identified as a possible source of information. It is this grammatical structure, along with some pattern matching to eliminate false positives, that is used in the approach taken in this work. These repair structures are identified within a word lattice and when found result in a SKIP being added to the lattice to allow the reparandum of the repair to be ignored during the hypothesis generation process. Word fragment information is included using a sub-word pattern matching process and cue phrases are also identified within the lattice and used in the repair detection process. These simple, yet effective, techniques have proved very successful in identifying and correcting speech-repairs in a number of evaluations performed on a speech recognition system incorporating the repair procedure. On an un-seen spontaneous lecture taken from the Durham corpus, using a dictionary of 2,275 words and phoneme corruption of 15%, the system achieved a correction recall rate of 72% and a correction precision rate of 75%.The achievements of the project include the automatic detection and correction of speech-repairs, including word fragments and cue phrases, in the sub-section of an automatic speech recognition system processing spontaneous speech

Characterizing phonetic transformations and fine-grained acoustic differences across dialects

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 169-175).This thesis is motivated by the gaps between speech science and technology in analyzing dialects. In speech science, investigating phonetic rules is usually manually laborious and time consuming, limiting the amount of data analyzed. Without sufficient data, the analysis could potentially overlook or over-specify certain phonetic rules. On the other hand, in speech technology such as automatic dialect recognition, phonetic rules are rarely modeled explicitly. While many applications do not require such knowledge to obtain good performance, it is beneficial to specifically model pronunciation patterns in certain applications. For example, users of language learning software can benefit from explicit and intuitive feedback from the computer to alter their pronunciation; in forensic phonetics, it is important that results of automated systems are justifiable on phonetic grounds. In this work, we propose a mathematical framework to analyze dialects in terms of (1) phonetic transformations and (2) acoustic differences. The proposed Phonetic based Pronunciation Model (PPM) uses a hidden Markov model to characterize when and how often substitutions, insertions, and deletions occur. In particular, clustering methods are compared to better model deletion transformations. In addition, an acoustic counterpart of PPM, Acoustic-based Pronunciation Model (APM), is proposed to characterize and locate fine-grained acoustic differences such as formant transitions and nasalization across dialects. We used three data sets to empirically compare the proposed models in Arabic and English dialects. Results in automatic dialect recognition demonstrate that the proposed models complement standard baseline systems. Results in pronunciation generation and rule retrieval experiments indicate that the proposed models learn underlying phonetic rules across dialects. Our proposed system postulates pronunciation rules to a phonetician who interprets and refines them to discover new rules or quantify known rules. This can be done on large corpora to develop rules of greater statistical significance than has previously been possible. Potential applications of this work include speaker characterization and recognition, automatic dialect recognition, automatic speech recognition and synthesis, forensic phonetics, language learning or accent training education, and assistive diagnosis tools for speech and voice disorders.by Nancy Fang-Yih Chen.Ph.D

A characterization of the problem of new, out-of-vocabulary words in continuous-speech recognition and understanding

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 167-173).by Irvine Lee Hetherington.Ph.D

Speech processing using digital MEMS microphones

The last few years have seen the start of a unique change in microphones for consumer devices such as smartphones or tablets. Almost all analogue capacitive microphones are being replaced by digital silicon microphones or MEMS microphones. MEMS microphones perform differently to conventional analogue microphones. Their greatest disadvantage is significantly increased self-noise or decreased SNR, while their most significant benefits are ease of design and manufacturing and improved sensitivity matching. This thesis presents research on speech processing, comparing conventional analogue microphones with the newly available digital MEMS microphones. Specifically, voice activity detection, speaker diarisation (who spoke when), speech separation and speech recognition are looked at in detail. In order to carry out this research different microphone arrays were built using digital MEMS microphones and corpora were recorded to test existing algorithms and devise new ones. Some corpora that were created for the purpose of this research will be released to the public in 2013. It was found that the most commonly used VAD algorithm in current state-of-theart diarisation systems is not the best-performing one, i.e. MLP-based voice activity detection consistently outperforms the more frequently used GMM-HMM-based VAD schemes. In addition, an algorithm was derived that can determine the number of active speakers in a meeting recording given audio data from a microphone array of known geometry, leading to improved diarisation results. Finally, speech separation experiments were carried out using different post-filtering algorithms, matching or exceeding current state-of-the art results. The performance of the algorithms and methods presented in this thesis was verified by comparing their output using speech recognition tools and simple MLLR adaptation and the results are presented as word error rates, an easily comprehensible scale. To summarise, using speech recognition and speech separation experiments, this thesis demonstrates that the significantly reduced SNR of the MEMS microphone can be compensated for with well established adaptation techniques such as MLLR. MEMS microphones do not affect voice activity detection and speaker diarisation performance