DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon

IF 4.2 1区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Transactions of the Association for Computational Linguistics Pub Date : 2022-06-22 DOI:10.1162/tacl_a_00505

Robin Algayres, Tristan Ricoul, Julien Karadayi, Hugo Laurenccon, Salah Zaiem, Abdel-rahman Mohamed, Benoît Sagot, E. Dupoux

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引用次数: 6

Abstract

Abstract Finding word boundaries in continuous speech is challenging as there is little or no equivalent of a ‘space’ delimiter between words. Popular Bayesian non-parametric models for text segmentation (Goldwater et al., 2006, 2009) use a Dirichlet process to jointly segment sentences and build a lexicon of word types. We introduce DP-Parse, which uses similar principles but only relies on an instance lexicon of word tokens, avoiding the clustering errors that arise with a lexicon of word types. On the Zero Resource Speech Benchmark 2017, our model sets a new speech segmentation state-of-the-art in 5 languages. The algorithm monotonically improves with better input representations, achieving yet higher scores when fed with weakly supervised inputs. Despite lacking a type lexicon, DP-Parse can be pipelined to a language model and learn semantic and syntactic representations as assessed by a new spoken word embedding benchmark. 1

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DP解析：使用实例词典从原始语音中查找单词边界

摘要在连续语音中寻找单词边界是一项挑战，因为单词之间几乎没有或根本没有“空格”分隔符。用于文本分割的流行贝叶斯非参数模型（Goldwater等人，20062009）使用狄利克雷过程来联合分割句子并构建单词类型的词典。我们介绍了DP Parse，它使用了类似的原理，但只依赖于单词标记的实例词典，避免了单词类型词典中出现的聚类错误。在2017年零资源语音基准测试上，我们的模型在5种语言中设置了最先进的新语音分割。该算法通过更好的输入表示进行单调改进，在使用弱监督输入时获得更高的分数。尽管缺乏类型词典，但DP Parse可以被流水线传输到语言模型，并通过新的口语单词嵌入基准来学习语义和句法表示。1.

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来源期刊

Transactions of the Association for Computational Linguistics Multiple-

CiteScore

32.60

自引率

4.60%

发文量

审稿时长

8 weeks

期刊介绍： The highly regarded quarterly journal Computational Linguistics has a companion journal called Transactions of the Association for Computational Linguistics. This open access journal publishes articles in all areas of natural language processing and is an important resource for academic and industry computational linguists, natural language processing experts, artificial intelligence and machine learning investigators, cognitive scientists, speech specialists, as well as linguists and philosophers. The journal disseminates work of vital relevance to these professionals on an annual basis.