Acoustic Data-Driven Grapheme-to-Phoneme Conversion in the Probabilistic Lexical Modeling Framework

One of the primary steps in building automatic speech recognition (ASR) and text-to-speech systems is the development of a phonemic lexicon that provides a mapping between each word and its pronunciation as a sequence of phonemes. Phoneme lexicons can be developed by humans through use of linguistic knowledge, however, this would be a costly and time-consuming task. To facilitate this process, grapheme-to phoneme conversion (G2P) techniques are used in which, given an initial phoneme lexicon, the relationship between graphemes and phonemes is learned through data-driven methods. This article presents a novel G2P formalism which learns the grapheme-to-phoneme relationship through acoustic data and potentially relaxes the need for an initial phonemic lexicon in the target language. The formalism involves a training part followed by an inference part. In the training part, the grapheme-to-phoneme relationship is captured in a probabilistic lexical modeling framework. In this framework, a hidden Markov model (HMM) is trained in which each HMM state representing a grapheme is parameterized by a categorical distribution of phonemes. Then in the inference part, given the orthographic transcription of the word and the learned HMM, the most probable sequence of phonemes is inferred. In this article, we show that the recently proposed acoustic G2P approach in the Kullback Leibler divergence-based HMM (KL-HMM) framework is a particular case of this formalism. We then benchmark the approach against two popular G2P approaches, namely joint multigram approach and decision tree-based approach. Our experimental studies on English and French show that despite relatively poor performance at the pronunciation level, the performance of the proposed approach is not significantly different than the state-of-the-art G2P methods at the ASR level. (C) 2016 Elsevier B.V. All rights reserved

Probabilistic Lexical Modeling and Grapheme-based Automatic Speech Recognition

Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems use phonemes as subword units. Thus, development of ASR system for a new language or domain depends upon the availability of a phoneme lexicon in the target language. In this paper, we introduce the notion of probabilistic lexical modeling and present an ASR approach where a) first, the relationship between acoustics and phonemes is learned on available acoustic and lexical resources (not necessarily from the target language or domain), and then b) probabilistic grapheme-to-phoneme relationship is learned using the acoustic data of targeted language or domain. The resulting system is a grapheme-based ASR system. This brings in two potential advantages. First, development of lexicon for target language or domain becomes easy i.e., creation of a grapheme lexicon where each word is transcribed by its orthography. Second, the ASR system can exploit both acoustic and lexical resources of multiple languages and domains. We evaluate and show the potential of the proposed approach through a) an in-domain study, where acoustic and lexical resources of target language or domain are used to build an ASR system, b) a monolingual cross-domain study, where acoustic and lexical resources of another domain are used to build an ASR system for a new domain, and c) a multilingual cross-domain study, where acoustic and lexical resources of multiple languages are used to build multi-accent non-native speech recognition system

Towards Weakly Supervised Acoustic Subword Unit Discovery and Lexicon Development Using Hidden Markov Models

Developing a phonetic lexicon for a language requires linguistic knowledge as well as human effort, which may not be available, particularly for under-resourced languages. An alternative to development of a phonetic lexicon is to automatically derive subword units using acoustic information and generate associated pronunciations. In the literature, this has been mostly studied from the pronunciation variation modeling perspective. In this article, we investigate automatic subword unit derivation from the under-resourced language point of view. Towards that, we present a novel hidden Markov model (HMM) formalism for automatic derivation of subword units and pronunciation generation using only transcribed speech data. In this approach, the subword units are derived from the clustered context-dependent units in a grapheme based system using the maximum-likelihood criterion. The subword unit based pronunciations are then generated either by deterministic or probabilistic learning of the relationship between the graphemes and the acoustic subword units (ASWUs). In this article, we first establish the proposed framework on a well resourced language by comparing it against related approaches in the literature and investigating the transferability of the derived subword units to other domains. We then show the scalability of the proposed approach on real under-resourced scenarios by conducting studies on Scottish Gaelic, a genuinely minority and endangered language, and comparing the approach against state-of-the-art grapheme-based approaches in under-resourced scenarios. Our experimental studies on English show that the derived subword units can not only lead to better ASR systems compared to graphemes, but can also be exploited to build out-of-domain ASR systems. The experimental studies on Scottish Gaelic show that the proposed ASWU-based lexicon development approach retains its dominance over grapheme-based lexicon. Alternately, the proposed approach yields significant gains in ASR performance, even when multilingual resources from resource-rich languages are exploited in the development of ASR systems

An HMM-Based Formalism for Automatic Subword Unit Derivation and Pronunciation Generation

We propose a novel hidden Markov model (HMM) formalism for automatic derivation of subword units and pronunciation generation using only transcribed speech data. In this approach, the subword units are derived from the clustered context-dependent units in a grapheme based system using maximum-likelihood criterion. The subword unit based pronunciations are then learned in the framework of Kullback-Leibler divergence based HMM. The automatic speech recognition (ASR) experiments on WSJ0 English corpus show that the approach leads to 12.7 % relative reduction in word error rate compared to grapheme-based system. Our approach can be bene-ficial in reducing the need for expert knowledge in development of ASR as well as text-to-speech systems. Index Terms — automatic subword unit derivation, pronuncia-tion generation, hidden Markov model, Kullback-Leibler divergence based hidden Markov model 1