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

New Grapheme Generation Rules for Two-Stage Modelbased Grapheme-to-Phoneme Conversion

The precise conversion of arbitrary text into its  corresponding phoneme sequence (grapheme-to-phoneme or G2P conversion) is implemented in speech synthesis and recognition, pronunciation learning software, spoken term detection and spoken document retrieval systems. Because the quality of this module plays an important role in the performance of such systems and many problems regarding G2P conversion have been reported, we propose a novel two-stage model-based approach, which is implemented using an existing weighted finite-state transducer-based G2P conversion framework, to improve the performance of the G2P conversion model. The first-stage model is built for automatic conversion of words  to phonemes, while  the second-stage  model utilizes the input graphemes and output phonemes obtained from the first stage to determine the best final output phoneme sequence. Additionally, we designed new grapheme generation rules, which enable extra detail for the vowel and consonant graphemes appearing within a word. When compared with previous approaches, the evaluation results indicate that our approach using rules focusing on the vowel graphemes slightly improved the accuracy of the out-of-vocabulary dataset and consistently increased the accuracy of the in-vocabulary dataset

Semi-supervised training for automatic speech recognition

State-of-the-art automatic speech recognition (ASR) systems use sequence-level objectives like Connectionist Temporal Classification (CTC) and Lattice-free Maximum Mutual Information (LF-MMI) for training neural network-based acoustic models. These methods are known to be most effective with large size datasets with hundreds or thousands of hours of data. It is difficult to obtain large amounts of supervised data other than in a few major languages like English and Mandarin. It is also difficult to obtain supervised data in a myriad of channel and envirormental conditions. On the other hand, large amounts of unsupervised audio can be obtained fairly easily. There are enormous amounts of unsupervised data available in broadcast TV, call centers and YouTube for many different languages and in many environment conditions. The goal of this research is to discover how to best leverage the available unsupervised data for training acoustic models for ASR. In the first part of this thesis, we extend the Maximum Mutual Information (MMI) training to the semi-supervised training scenario. We show that maximizing Negative Conditional Entropy (NCE) over lattices from unsupervised data, along with state-level Minimum Bayes Risk (sMBR) on supervised data, in a multi-task architecture gives word error rate (WER) improvements without needing any confidence-based filtering. In the second part of this thesis, we investigate using lattice-based supervision as numerator graph to incorporate uncertainities in unsupervised data in the LF-MMI training framework. We explore various aspects of creating the numerator graph including splitting lattices for minibatch training, applying tolerance to frame-level alignments, pruning beam sizes, word LM scale and inclusion of pronunciation variants. We show that the WER recovery rate (WRR) of our proposed approach is 5-10\% absolute better than that of the baseline of using 1-best transcript as supervision, and is stable in the 40-60\% range even on large-scale setups and multiple different languages. Finally, we explore transfer learning for the scenario where we have unsupervised data in a mismatched domain. First, we look at the teacher-student learning approach for cases where parallel data is available in source and target domains. Here, we train a "student" neural network on the target domain to mimic a "teacher" neural network on the source domain data, but using sequence-level posteriors instead of the traditional approach of using frame-level posteriors. We show that the proposed approach is very effective to deal with acoustic domain mismatch in multiple scenarios of unsupervised domain adaptation -- clean to noisy speech, 8kHz to 16kHz speech, close-talk microphone to distant microphone. Second, we investigate approaches to mitigate language domain mismatch, and show that a matched language model significantly improves WRR. We finally show that our proposed semi-supervised transfer learning approach works effectively even on large-scale unsupervised datasets with 2000 hours of audio in natural and realistic conditions

Multi-dialect Arabic broadcast speech recognition

Dialectal Arabic speech research suffers from the lack of labelled resources and standardised orthography. There are three main challenges in dialectal Arabic speech recognition: (i) finding labelled dialectal Arabic speech data, (ii) training robust dialectal speech recognition models from limited labelled data and (iii) evaluating speech recognition for dialects with no orthographic rules. This thesis is concerned with the following three contributions: Arabic Dialect Identification: We are mainly dealing with Arabic speech without prior knowledge of the spoken dialect. Arabic dialects could be sufficiently diverse to the extent that one can argue that they are different languages rather than dialects of the same language. We have two contributions: First, we use crowdsourcing to annotate a multi-dialectal speech corpus collected from Al Jazeera TV channel. We obtained utterance level dialect labels for 57 hours of high-quality consisting of four major varieties of dialectal Arabic (DA), comprised of Egyptian, Levantine, Gulf or Arabic peninsula, North African or Moroccan from almost 1,000 hours. Second, we build an Arabic dialect identification (ADI) system. We explored two main groups of features, namely acoustic features and linguistic features. For the linguistic features, we look at a wide range of features, addressing words, characters and phonemes. With respect to acoustic features, we look at raw features such as mel-frequency cepstral coefficients combined with shifted delta cepstra (MFCC-SDC), bottleneck features and the i-vector as a latent variable. We studied both generative and discriminative classifiers, in addition to deep learning approaches, namely deep neural network (DNN) and convolutional neural network (CNN). In our work, we propose Arabic as a five class dialect challenge comprising of the previously mentioned four dialects as well as modern standard Arabic. Arabic Speech Recognition: We introduce our effort in building Arabic automatic speech recognition (ASR) and we create an open research community to advance it. This section has two main goals: First, creating a framework for Arabic ASR that is publicly available for research. We address our effort in building two multi-genre broadcast (MGB) challenges. MGB-2 focuses on broadcast news using more than 1,200 hours of speech and 130M words of text collected from the broadcast domain. MGB-3, however, focuses on dialectal multi-genre data with limited non-orthographic speech collected from YouTube, with special attention paid to transfer learning. Second, building a robust Arabic ASR system and reporting a competitive word error rate (WER) to use it as a potential benchmark to advance the state of the art in Arabic ASR. Our overall system is a combination of five acoustic models (AM): unidirectional long short term memory (LSTM), bidirectional LSTM (BLSTM), time delay neural network (TDNN), TDNN layers along with LSTM layers (TDNN-LSTM) and finally TDNN layers followed by BLSTM layers (TDNN-BLSTM). The AM is trained using purely sequence trained neural networks lattice-free maximum mutual information (LFMMI). The generated lattices are rescored using a four-gram language model (LM) and a recurrent neural network with maximum entropy (RNNME) LM. Our official WER is 13%, which has the lowest WER reported on this task. Evaluation: The third part of the thesis addresses our effort in evaluating dialectal speech with no orthographic rules. Our methods learn from multiple transcribers and align the speech hypothesis to overcome the non-orthographic aspects. Our multi-reference WER (MR-WER) approach is similar to the BLEU score used in machine translation (MT). We have also automated this process by learning different spelling variants from Twitter data. We mine automatically from a huge collection of tweets in an unsupervised fashion to build more than 11M n-to-m lexical pairs, and we propose a new evaluation metric: dialectal WER (WERd). Finally, we tried to estimate the word error rate (e-WER) with no reference transcription using decoding and language features. We show that our word error rate estimation is robust for many scenarios with and without the decoding features

GREC: Multi-domain Speech Recognition for the Greek Language

Μία από τις κορυφαίες προκλήσεις στην Αυτόματη Αναγνώριση Ομιλίας είναι η ανάπτυξη ικανών συστημάτων που μπορούν να έχουν ισχυρή απόδοση μέσα από διαφορετικές συνθήκες ηχογράφησης. Στο παρόν έργο κατασκευάζουμε και αναλύουμε το GREC, μία μεγάλη πολυτομεακή συλλογή δεδομένων για αυτόματη αναγνώριση ομιλίας στην ελληνική γλώσσα. Το GREC αποτελείται από τρεις βάσεις δεδομένων στους θεματικούς τομείς των «εκπομπών ειδήσεων», «ομιλίας από δωρισμένες εγγραφές φωνής», «ηχητικών βιβλίων» και μιας νέας συλλογής δεδομένων στον τομέα των «πολιτικών ομιλιών». Για τη δημιουργία του τελευταίου, συγκεντρώνουμε δεδομένα ομιλίας από ηχογραφήσεις των επίσημων συνεδριάσεων της Βουλής των Ελλήνων, αποδίδοντας ένα σύνολο δεδομένων που αποτελείται από 120 ώρες ομιλίας πολιτικού περιεχομένου. Περιγράφουμε με λεπτομέρεια την καινούρια συλλογή δεδομένων, την προεπεξεργασία και την ευθυγράμμιση ομιλίας, τα οποία βασίζονται στο εργαλείο ανοιχτού λογισμικού Kaldi. Επιπλέον, αξιολογούμε την απόδοση των μοντέλων Gaussian Mixture (GMM) - Hidden Markov (HMM) και Deep Neural Network (DNN) - HMM όταν εφαρμόζονται σε δεδομένα από διαφορετικούς τομείς. Τέλος, προσθέτουμε τη δυνατότητα αυτόματης δεικτοδότησης ομιλητών στο Kaldi-gRPC-Server, ενός εργαλείου γραμμένο σε Python που βασίζεται στο PyKaldi και στο gRPC για βελτιωμένη ανάπτυξη μοντέλων αυτόματης αναγνώρισης ομιλίας.One of the leading challenges in Automatic Speech Recognition (ASR) is the development of robust systems that can perform well under multiple settings. In this work we construct and analyze GREC, a large, multi-domain corpus for automatic speech recognition for the Greek language. GREC is a collection of three available subcorpora over the domains of “news casts”, “crowd-sourced speech”, “audiobooks”, and a new corpus in the domain of “public speeches”. For the creation of the latter, HParl, we collect speech data from recordings of the official proceedings of the Hellenic Parliament, yielding, a dataset which consists of 120 hours of political speech segments. We describe our data collection, pre-processing and alignment setup, which are based on Kaldi toolkit. Furthermore, we perform extensive ablations on the recognition performance of Gaussian Mixture (GMM) - Hidden Markov (HMM) models and Deep Neural Network (DNN) - HMM models over the different domains. Finally, we integrate speaker diarization features to Kaldi-gRPC-Server, a modern, pythonic tool based on PyKaldi and gRPC for streamlined deployment of Kaldi based speech recognition

Exploiting foreign resources for DNN-based ASR

Manual transcription of audio databases for the development of automatic speech recognition (ASR) systems is a costly and time-consuming process. In the context of deriving acoustic models adapted to a specific application, or in low-resource scenarios, it is therefore essential to explore alternatives capable of improving speech recognition results. In this paper, we investigate the relevance of foreign data characteristics, in particular domain and language, when using this data as an auxiliary data source for training ASR acoustic models based on deep neural networks (DNNs). The acoustic models are evaluated on a challenging bilingual database within the scope of the MediaParl project. Experimental results suggest that in-language (but out-of-domain) data is more beneficial than in-domain (but out-of-language) data when employed in either supervised or semi-supervised training of DNNs. The best performing ASR system, an HMM/GMM acoustic model that exploits DNN as a discriminatively trained feature extractor outperforms the best performing HMM/DNN hybrid by about 5 % relative (in terms of WER). An accumulated relative gain with respect to the MFCC-HMM/GMM baseline is about 30 % WER

Pronunciation modelling in end-to-end text-to-speech synthesis

Sequence-to-sequence (S2S) models in text-to-speech synthesis (TTS) can achieve high-quality naturalness scores without extensive processing of text-input. Since S2S models have been proposed in multiple aspects of the TTS pipeline, the field has focused on embedding the pipeline toward End-to-End (E2E-) TTS where a waveform is predicted directly from a sequence of text or phone characters. Early work on E2ETTS in English, such as Char2Wav [1] and Tacotron [2], suggested that phonetisation (lexicon-lookup and/or G2P modelling) could be implicitly learnt in a text-encoder during training. The benefits of a learned text encoding include improved modelling of phonetic context, which make contextual linguistic features traditionally used in TTS pipelines redundant [3]. Subsequent work on E2E-TTS has since shown similar naturalness scores with text- or phone-input (e.g. as in [4]). Successful modelling of phonetic context has led some to question the benefit of using phone- instead of text-input altogether (see [5]). The use of text-input brings into question the value of the pronunciation lexicon in E2E-TTS. Without phone-input, a S2S encoder learns an implicit grapheme-tophoneme (G2P) model from text-audio pairs during training. With common datasets for E2E-TTS in English, I simulated implicit G2P models, finding increased error rates compared to a traditional, lexicon-based G2P model. Ultimately, successful G2P generalisation is difficult for some words (e.g. foreign words and proper names) since the knowledge to disambiguate their pronunciations may not be provided by the local grapheme context and may require knowledge beyond that contained in sentence-level text-audio sequences. When test stimuli were selected according to G2P difficulty, increased mispronunciations in E2E-TTS with text-input were observed. Following the proposed benefits of subword decomposition in S2S modelling in other language tasks (e.g. neural machine translation), the effects of morphological decomposition were investigated on pronunciation modelling. Learning of the French post-lexical phenomenon liaison was also evaluated. With the goal of an inexpensive, large-scale evaluation of pronunciation modelling, the reliability of automatic speech recognition (ASR) to measure TTS intelligibility was investigated. A re-evaluation of 6 years of results from the Blizzard Challenge was conducted. ASR reliably found similar significant differences between systems as paid listeners in controlled conditions in English. An analysis of transcriptions for words exhibiting difficult-to-predict G2P relations was also conducted. The E2E-ASR Transformer model used was found to be unreliable in its transcription of difficult G2P relations due to homophonic transcription and incorrect transcription of words with difficult G2P relations. A further evaluation of representation mixing in Tacotron finds pronunciation correction is possible when mixing text- and phone-inputs. The thesis concludes that there is still a place for the pronunciation lexicon in E2E-TTS as a pronunciation guide since it can provide assurances that G2P generalisation cannot