Acoustic data-driven lexicon learning based on a greedy pronunciation selection framework
Speech recognition systems for irregularly-spelled languages like English normally require hand-written pronunciations. In this paper, we describe a system for automatically obtaining pronunciations of words for which pronunciations are not available, but for which transcribed data exists. Our method integrates information from the letter sequence and from the acoustic evidence. The novel aspect of the problem that we address is the problem of how to prune entries from such a lexicon (since, empirically, lexicons with too many entries do not tend to be good for ASR performance). Experiments on various ASR tasks show that, with the proposed framework, starting with an initial lexicon of several thousand words, we are able to learn a lexicon which performs close to a full expert lexicon in terms of WER performance on test data, and is better than lexicons built using G2P alone or with a pruning criterion based on pronunciation probability.
Code (0)
등록된 구현이 없습니다.
Tasks
speech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
Automatic Text Pronunciation Correlation Generation and Application for Contextual Biasing
Effectively distinguishing the pronunciation correlations between different written texts is a significant issue in linguistic acoustics. Traditionally, such pronunciation correlations are obtained through manually desig…
Automatic Speech Recognitionspeech-recognitionSpeech RecognitionPronouncUR: An Urdu Pronunciation Lexicon Generator
State-of-the-art speech recognition systems rely heavily on three basic components: an acoustic model, a pronunciation lexicon and a language model. To build these components, a researcher needs linguistic as well as tec…
Grapheme-to-Phoneme ConversionLanguage ModelingLanguage Modellingspeech-recognition+1Pronunciation Generation for Foreign Language Words in Intra-Sentential Code-Switching Speech Recognition
Code-Switching refers to the phenomenon of switching languages within a sentence or discourse. However, limited code-switching , different language phoneme-sets and high rebuilding costs throw a challenge to make the spe…
Sentencespeech-recognitionSpeech RecognitionNo Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acou…
Language ModelingLanguage ModellingJira: a Kurdish Speech Recognition System Designing and Building Speech Corpus and Pronunciation Lexicon
In this paper, we introduce the first large vocabulary speech recognition system (LVSR) for the Central Kurdish language, named Jira. The Kurdish language is an Indo-European language spoken by more than 30 million peopl…
Language ModellingSentencespeech-recognitionSpeech Recognition