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

Language technologies in speech-enabled second language learning games : from reading to dialogue

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 237-244).Second language learning has become an important societal need over the past decades. Given that the number of language teachers is far below demand, computer-aided language learning software is becoming a promising supplement to traditional classroom learning, as well as potentially enabling new opportunities for self-learning. The use of speech technologies is especially attractive to offer students unlimited chances for speaking exercises. To create helpful and intelligent speaking exercises on a computer, it is necessary for the computer to not only recognize the acoustics, but also to understand the meaning and give appropriate responses. Nevertheless, most existing speech-enabled language learning software focuses only on speech recognition and pronunciation training. Very few have emphasized exercising the student's composition and comprehension abilities and adopting language technologies to enable free-form conversation emulating a real human tutor. This thesis investigates the critical functionalities of a computer-aided language learning system, and presents a generic framework as well as various language- and domain-independent modules to enable building complex speech-based language learning systems. Four games have been designed and implemented using the framework and the modules to demonstrate their usability and flexibility, where dynamic content creation, automatic assessment, and automatic assistance are emphasized. The four games, reading, translation, question-answering and dialogue, offer different activities with gradually increasing difficulty, and involve a wide range of language processing techniques, such as language understanding, language generation, question generation, context resolution, dialogue management and user simulation. User studies with real subjects show that the systems were well received and judged to be helpful.by Yushi Xu.Ph.D

Deep Learning for Distant Speech Recognition

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence. Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field. This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called network of deep neural networks. The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.Comment: PhD Thesis Unitn, 201

Dynamic language modeling for European Portuguese

Doutoramento em Engenharia InformáticaActualmente muitas das metodologias utilizadas para transcrição e indexação de transmissões noticiosas são baseadas em processos manuais. Com o processamento e transcrição deste tipo de dados os prestadores de serviços noticiosos procuram extrair informação semântica que permita a sua interpretação, sumarização, indexação e posterior disseminação selectiva. Pelo que, o desenvolvimento e implementação de técnicas automáticas para suporte deste tipo de tarefas têm suscitado ao longo dos últimos anos o interesse pela utilização de sistemas de reconhecimento automático de fala. Contudo, as especificidades que caracterizam este tipo de tarefas, nomeadamente a diversidade de tópicos presentes nos blocos de notícias, originam um elevado número de ocorrência de novas palavras não incluídas no vocabulário finito do sistema de reconhecimento, o que se traduz negativamente na qualidade das transcrições automáticas produzidas pelo mesmo. Para línguas altamente flexivas, como é o caso do Português Europeu, este problema torna-se ainda mais relevante. Para colmatar este tipo de problemas no sistema de reconhecimento, várias abordagens podem ser exploradas: a utilização de informações específicas de cada um dos blocos noticiosos a ser transcrito, como por exemplo os scripts previamente produzidos pelo pivot e restantes jornalistas, e outro tipo de fontes como notícias escritas diariamente disponibilizadas na Internet. Este trabalho engloba essencialmente três contribuições: um novo algoritmo para selecção e optimização do vocabulário, utilizando informação morfosintáctica de forma a compensar as diferenças linguísticas existentes entre os diferentes conjuntos de dados; uma metodologia diária para adaptação dinâmica e não supervisionada do modelo de linguagem, utilizando múltiplos passos de reconhecimento; metodologia para inclusão de novas palavras no vocabulário do sistema, mesmo em situações de não existência de dados de adaptação e sem necessidade re-estimação global do modelo de linguagem.Most of today methods for transcription and indexation of broadcast audio data are manual. Broadcasters process thousands hours of audio and video data on a daily basis, in order to transcribe that data, to extract semantic information, and to interpret and summarize the content of those documents. The development of automatic and efficient support for these manual tasks has been a great challenge and over the last decade there has been a growing interest in the usage of automatic speech recognition as a tool to provide automatic transcription and indexation of broadcast news and random and relevant access to large broadcast news databases. However, due to the common topic changing over time which characterizes this kind of tasks, the appearance of new events leads to high out-of-vocabulary (OOV) word rates and consequently to degradation of recognition performance. This is especially true for highly inflected languages like the European Portuguese language. Several innovative techniques can be exploited to reduce those errors. The use of news shows specific information, such as topic-based lexicons, pivot working script, and other sources such as the online written news daily available in the Internet can be added to the information sources employed by the automatic speech recognizer. In this thesis we are exploring the use of additional sources of information for vocabulary optimization and language model adaptation of a European Portuguese broadcast news transcription system. Hence, this thesis has 3 different main contributions: a novel approach for vocabulary selection using Part-Of-Speech (POS) tags to compensate for word usage differences across the various training corpora; language model adaptation frameworks performed on a daily basis for single-stage and multistage recognition approaches; a new method for inclusion of new words in the system vocabulary without the need of additional data or language model retraining