Fast and Accurate OOV Decoder on High-Level Features

This work proposes a novel approach to out-of-vocabulary (OOV) keyword search (KWS) task. The proposed approach is based on using high-level features from an automatic speech recognition (ASR) system, so called phoneme posterior based (PPB) features, for decoding. These features are obtained by calculating time-dependent phoneme posterior probabilities from word lattices, followed by their smoothing. For the PPB features we developed a special novel very fast, simple and efficient OOV decoder. Experimental results are presented on the Georgian language from the IARPA Babel Program, which was the test language in the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum term weighted value (MTWV) metric and computational speed, for single ASR systems, the proposed approach significantly outperforms the state-of-the-art approach based on using in-vocabulary proxies for OOV keywords in the indexed database. The comparison of the two OOV KWS approaches on the fusion results of the nine different ASR systems demonstrates that the proposed OOV decoder outperforms the proxy-based approach in terms of MTWV metric given the comparable processing speed. Other important advantages of the OOV decoder include extremely low memory consumption and simplicity of its implementation and parameter optimization.Comment: Interspeech 2017, August 2017, Stockholm, Sweden. 201

Strategies for Handling Out-of-Vocabulary Words in Automatic Speech Recognition

Nowadays, most ASR (automatic speech recognition) systems deployed in industry are closed-vocabulary systems, meaning we have a limited vocabulary of words the system can recognize, and where pronunciations are provided to the system. Words out of this vocabulary are called out-of-vocabulary (OOV) words, for which either pronunciations or both spellings and pronunciations are not known to the system. The basic motivations of developing strategies to handle OOV words are: First, in the training phase, missing or wrong pronunciations of words in training data results in poor acoustic models. Second, in the test phase, words out of the vocabulary cannot be recognized at all, and mis-recognition of OOV words may affect recognition performance of its in-vocabulary neighbors as well. Therefore, this dissertation is dedicated to exploring strategies of handling OOV words in closed-vocabulary ASR. First, we investigate dealing with OOV words in ASR training data, by introducing an acoustic-data driven pronunciation learning framework using a likelihood-reduction based criterion for selecting pronunciation candidates from multiple sources, i.e. standard grapheme-to-phoneme algorithms (G2P) and phonetic decoding, in a greedy fashion. This framework effectively expands a small hand-crafted pronunciation lexicon to cover OOV words, for which the learned pronunciations have higher quality than approaches using G2P alone or using other baseline pruning criteria. Furthermore, applying the proposed framework to generate alternative pronunciations for in-vocabulary (IV) words improves both recognition performance on relevant words and overall acoustic model performance. Second, we investigate dealing with OOV words in ASR test data, i.e. OOV detection and recovery. We first conduct a comparative study of a hybrid lexical model (HLM) approach for OOV detection, and several baseline approaches, with the conclusion that the HLM approach outperforms others in both OOV detection and first pass OOV recovery performance. Next, we introduce a grammar-decoding framework for efficient second pass OOV recovery, showing that with properly designed schemes of estimating OOV unigram probabilities, the framework significantly improves OOV recovery and overall decoding performance compared to first pass decoding. Finally we propose an open-vocabulary word-level recurrent neural network language model (RNNLM) re-scoring framework, making it possible to re-score lattices containing recovered OOVs using a single word-level RNNLM, that was ignorant of OOVs when it was trained. Above all, the whole OOV recovery pipeline shows the potential of a highly efficient open-vocabulary word-level ASR decoding framework, tightly integrated into a standard WFST decoding pipeline

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Acoustic Modelling for Under-Resourced Languages

Automatic speech recognition systems have so far been developed only for very few languages out of the 4,000-7,000 existing ones. In this thesis we examine methods to rapidly create acoustic models in new, possibly under-resourced languages, in a time and cost effective manner. For this we examine the use of multilingual models, the application of articulatory features across languages, and the automatic discovery of word-like units in unwritten languages

Pronunciation modeling for Cantonese speech recognition.

Kam Patgi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaf 103).Abstracts in English and Chinese.Chapter Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Automatic Speech Recognition --- p.1Chapter 1.2 --- Pronunciation Modeling in ASR --- p.2Chapter 1.3 --- Obj ectives of the Thesis --- p.5Chapter 1.4 --- Thesis Outline --- p.5Reference --- p.7Chapter Chapter 2. --- The Cantonese Dialect --- p.9Chapter 2.1 --- Cantonese - A Typical Chinese Dialect --- p.10Chapter 2.1.1 --- Cantonese Phonology --- p.11Chapter 2.1.2 --- Cantonese Phonetics --- p.12Chapter 2.2 --- Pronunciation Variation in Cantonese --- p.13Chapter 2.2.1 --- Phone Change and Sound Change --- p.14Chapter 2.2.2 --- Notation for Different Sound Units --- p.16Chapter 2.3 --- Summary --- p.17Reference --- p.18Chapter Chapter 3. --- Large-Vocabulary Continuous Speech Recognition for Cantonese --- p.19Chapter 3.1 --- Feature Representation of the Speech Signal --- p.20Chapter 3.2 --- Probabilistic Framework of ASR --- p.20Chapter 3.3 --- Hidden Markov Model for Acoustic Modeling --- p.21Chapter 3.4 --- Pronunciation Lexicon --- p.25Chapter 3.5 --- Statistical Language Model --- p.25Chapter 3.6 --- Decoding --- p.26Chapter 3.7 --- The Baseline Cantonese LVCSR System --- p.26Chapter 3.7.1 --- System Architecture --- p.26Chapter 3.7.2 --- Speech Databases --- p.28Chapter 3.8 --- Summary --- p.29Reference --- p.30Chapter Chapter 4. --- Pronunciation Model --- p.32Chapter 4.1 --- Pronunciation Modeling at Different Levels --- p.33Chapter 4.2 --- Phone-level pronunciation model and its Application --- p.35Chapter 4.2.1 --- IF Confusion Matrix (CM) --- p.35Chapter 4.2.2 --- Decision Tree Pronunciation Model (DTPM) --- p.38Chapter 4.2.3 --- Refinement of Confusion Matrix --- p.41Chapter 4.3 --- Summary --- p.43References --- p.44Chapter Chapter 5. --- Pronunciation Modeling at Lexical Level --- p.45Chapter 5.1 --- Construction of PVD --- p.46Chapter 5.2 --- PVD Pruning by Word Unigram --- p.48Chapter 5.3 --- Recognition Experiments --- p.49Chapter 5.3.1 --- Experiment 1 ´ؤPronunciation Modeling in LVCSR --- p.49Chapter 5.3.2 --- Experiment 2 ´ؤ Pronunciation Modeling in Domain Specific task --- p.58Chapter 5.3.3 --- Experiment 3 ´ؤ PVD Pruning by Word Unigram --- p.62Chapter 5.4 --- Summary --- p.63Reference --- p.64Chapter Chapter 6. --- Pronunciation Modeling at Acoustic Model Level --- p.66Chapter 6.1 --- Hierarchy of HMM --- p.67Chapter 6.2 --- Sharing of Mixture Components --- p.68Chapter 6.3 --- Adaptation of Mixture Components --- p.70Chapter 6.4 --- Combination of Mixture Component Sharing and Adaptation --- p.74Chapter 6.5 --- Recognition Experiments --- p.78Chapter 6.6 --- Result Analysis --- p.80Chapter 6.6.1 --- Performance of Sharing Mixture Components --- p.81Chapter 6.6.2 --- Performance of Mixture Component Adaptation --- p.84Chapter 6.7 --- Summary --- p.85Reference --- p.87Chapter Chapter 7. --- Pronunciation Modeling at Decoding Level --- p.88Chapter 7.1 --- Search Process in Cantonese LVCSR --- p.88Chapter 7.2 --- Model-Level Search Space Expansion --- p.90Chapter 7.3 --- State-Level Output Probability Modification --- p.92Chapter 7.4 --- Recognition Experiments --- p.93Chapter 7.4.1 --- Experiment 1 ´ؤModel-Level Search Space Expansion --- p.93Chapter 7.4.2 --- Experiment 2 ´ؤ State-Level Output Probability Modification …… --- p.94Chapter 7.5 --- Summary --- p.96Reference --- p.97Chapter Chapter 8. --- Conclusions and Suggestions for Future Work --- p.98Chapter 8.1 --- Conclusions --- p.98Chapter 8.2 --- Suggestions for Future Work --- p.100Reference --- p.103Appendix I Base Syllable Table --- p.104Appendix II Cantonese Initials and Finals --- p.105Appendix III IF confusion matrix --- p.106Appendix IV Phonetic Question Set --- p.112Appendix V CDDT and PCDT --- p.11