JOINT-SEQUENCE MODELS FOR GRAPHEME-TO-PHONEME CONVERSION PDF

We'd like to understand how you use our websites in order to improve them. Register your interest. A model to convert a grapheme into a phoneme G2P is crucial in the natural language processing area. In general, it is developed using a probabilistic-based data-driven approach and directly applied to a sequence of graphemes with no other information.

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Joint-sequence models for grapheme-to-phoneme conversion Published on May 1, in Speech Communication 1. Maximilian Bisani 8 Estimated H-index: 8. Estimated H-index: Find in Lib. Add to Collection. Grapheme-to-phoneme conversion is the task of finding the pronunciation of a word given its written form. It has important applications in text-to-speech and speech recognition. Joint-sequence models are a simple and theoretically stringent probabilistic framework that is applicable to this problem.

This article provides a self-contained and detailed description of this method. We present a novel estimation algorithm and demonstrate high accuracy on a variety of databases. Moreover, we study the impact of the maximum approximation in training and transcription, the interaction of model size parameters, n-best list generation, confidence measures, and phoneme-to-grapheme conversion.

Our software implementation of the method proposed in this work is available under an Open Source license. References 48 Citations Cite. The Kaldi Speech Recognition Toolkit. Read Later. After signing in, all features are FREE. References The RWTH systems apply a two pass search strategy with a fourgram one-pass decoder including a fast vocal tract length normalization variant as first pass.

The sys Unsupervised, language-independent grapheme-to-phoneme conversion by latent analogy. Bellegarda Apple Inc. H-Index: Automatic, data-driven grapheme-to-phoneme conversion is a challenging but often necessary task. The top-down strategy implicitly followed by traditional inductive learning techniques tends to dismiss relevant contexts when they have been seen too infrequently in the training data.

The bottom-up philosophy inherent in pronunciation by analogy allows for a markedly better handling of unusual patterns, but also relies heavily on individual, language-dependent alignments between letters and phoneme Hermann Ney H-Index: Furthermore, we present first recognition results on the English speech recordings. The transcription system has been derived from an older speech recognition system built for the North-American broadcast news task.

We report on the measures taken for rapid cross-doma Open vocabulary speech recognition with flat hybrid models. However, many important speech recognition tasks feature an open, constantly changing vocabulary.

Ideally, a system designed for such open vocabulary tasks would be able to recognize arbitrary, even previously unseen words. To some extent this can be achieved by using sub-lexical language models.

We demonstrate Bootstrap estimates for confidence intervals in ASR performance evaluation. Bisani H-Index: 1. The field of speech recognition has clearly benefited from precisely defined testing conditions and objective performance measures such as word error rate.

In the development and evaluation of new methods, the question arises whether the empirically observed difference in performance is due to a genuine advantage of one system over the other, or just an effect of chance.

However, many publications still do not concern themselves with the statistical significance of the results reported. We prese Assessing text-to-phoneme mapping strategies in speaker independent isolated word recognition.

Abstract A phonetic transcription of the vocabulary, i. Decision trees and neural networks have successfully been used for creating lexicons on-line from an open vocabulary. We briefly review these methods and compare them in detail in the text-to-phoneme mapping task as part of a phoneme based speaker independent speech recognizer. The decision tree and neural network based methods were first evaluated in t A systematic comparison of various statistical alignment models.

We present and compare various methods for computing word alignments using statistical or heuristic models. We consider the five alignment models presented in Brown, Della Pietra, Della Pietra, and Mercer , the hidden Markov alignment model, smoothing techniques, and refinements.

These statistical models are compared with two heuristic models based on the Dice coefficient. We present different methods for combining word alignments to perform a symmetrization of directed statistical alignme Conditional and joint models for grapheme-to-phoneme conversion.

Many important speech recognition tasks feature an open, constantly changing vocabulary. Recognition of new words requires acoustic baseforms for them to be known. Commonly words are transcribed manually, which poses a major burden on vocabulary adaptation and interdomain portability. In this work we investigate the possibility of applying a data-driven grapheme-tophoneme converter to obtain the necessary phonetic transcrip Recognition of out-of-vocabulary words with sub-lexical language models.

Cited By An investigation of phone-based subword units for end-to-end speech recognition. Richard Socher SF: Salesforce. Phones and their context-dependent variants have been the standard modeling units for conventional speech recognition systems, while characters and character-based subwords are becoming increasingly popular for end-to-end recognition systems. We investigate the use of phone-based subwords, and byte pair encoding BPE in particular, as modeling units for end-to-end speech recognition, and develop multi-level language model-based decoding algorithms based on a pronunciation dictionary.

Besides th Claudiu Musat Swisscom H-Index: 7. We introduce a dictionary containing forms of common words in various Swiss German dialects normalized into High German. As Swiss German is, for now, a predominantly spoken language, there is a significant variation in the written forms, even between speakers of the same dialect. This dictionary becomes thus the first reso Marelie H. We present the design and development of a South African directory enquiries corpus.

It contains audio and orthographic transcriptions of a wide range of South African names produced by first-language speakers of four languages, namely Afrikaans, English, isiZulu and Sesotho.

Dealing with the unknown — addressing challenges in evaluating unintelligible speech. ABSTRACTWhen investigating the interaction between speech production and intelligibility, unintelligible speech portions are often of particular interest. Therefore, the fact that the standard quan Assessing the accuracy of existing forced alignment software on varieties of British English.

Polyphone disambiguation aims to select the correct pronunciation for a polyphonic word from several candidates, which is important for text-to-speech synthesis. Since the pronunciation of a polyphonic word is usually decided by its context, polyphone disambiguation can be regarded as a language understanding task. However, BERT models are usually too heavy to Yao Liu. It is difficult for a language model LM to perform well with limited in-domain transcripts in low-resource speech recognition.

In this paper, we mainly summarize and extend some effective methods to make the most of the out-of-domain data to improve LMs. These methods include data selection, vocabulary expansion, lexicon augmentation, multi-model fusion and so on. The methods are integrated into a systematic procedure, which proves to be effective for improving both n-gram and neural network L An encoder-decoder based grapheme-to-phoneme converter for Bangla speech synthesis.

Mohammad Shahidur Rahman H-Index: 1. Tao Ma H-Index: 1. Self-attention has been a huge success for many downstream tasks in NLP, which led to exploration of applying self-attention to speech problems as well.

The efficacy of self-attention in speech applications, however, seems not fully blown yet since it is challenging to handle highly correlated speech frames in the context of self-attention. In this paper we propose a new neural network model architecture, namely multi-stream self-attention, to address the issue thus make the self-attention mecha Grapheme to phoneme conversion is the production of pronunciation for a given word.

Neural sequence to sequence models have been applied for grapheme to phoneme conversion recently. This paper analyzes the effectiveness of neural sequence to sequence models in grapheme to phoneme conversion for Myanmar language.

The first large Myanmar pronunciation dictionary is introduced, and it is applied in building sequence to sequence models. The performance of four grapheme to phoneme conversion models,

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Incorporating syllabification points into a model of grapheme-to-phoneme conversion

Grapheme-to-phoneme G2P conversion is an important task in automatic speech recognition and text-to-speech systems. However, previous works do not consider the practical issues when deploying G2P model in the production system, such as how to leverage additional unlabeled data to boost the accuracy, as well as reduce model size for online deployment. In this work, we propose token-level ensemble distillation for G2P conversion, which can 1 boost the accuracy by distilling the knowledge from additional unlabeled data, and 2 reduce the model size but maintain the high accuracy, both of which are very practical and helpful in the online production system. We use token-level knowledge distillation, which results in better accuracy than the sequence-level counterpart. Experiments on the publicly available CMUDict dataset and an internal English dataset demonstrate the effectiveness of our proposed method.

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Joint-sequence models for grapheme-to-phoneme conversion

Joint-sequence models for grapheme-to-phoneme conversion Published on May 1, in Speech Communication 1. Maximilian Bisani 8 Estimated H-index: 8. Estimated H-index: Find in Lib.

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Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion

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