Word segmentation through cross-lingual word-to-phoneme alignment

Stahlberg, Felix; Schlippe, Tim; Vogel, Stephan; Schultz, Tanja

doi:10.1109/slt.2012.6424202

Cited by 17 publications

(17 citation statements)

References 14 publications

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“…Word discovery experiments from text input on Mboshi were reported in [28]. Bilingual setups (cross-lingual supervision) for word segmentation were discussed by [29,30,31,9], but applied to speech transcripts (true phones). Looking at NMT from speech, the research by [11,12] are recent examples of approaches to end-to-end spoken language translation, but us- ing much larger data conditions than ours.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Word Segmentation from Speech with Attention

Godard¹,

Boito²,

Ondel³

et al. 2018

Interspeech 2018

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We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL). Our methodology assumes a pairing between recordings in the UL with translations in a well-resourced language. It uses Acoustic Unit Discovery (AUD) to convert speech into a sequence of pseudo-phones that is segmented using neural soft-alignments produced by a neural machine translation model. Evaluation uses an actual Bantu UL, Mboshi; comparisons to monolingual and bilingual baselines illustrate the potential of attentional word segmentation for language documentation. Index Terms:computational language documentation, encoder-decoder models, attentional models, unsupervised word segmentation.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Word Segmentation from Speech with Attention

Godard¹,

Boito²,

Ondel³

et al. 2018

Interspeech 2018

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“…This semi-supervised task lies between speech translation and keyword spotting, with cross-lingual supervision being used for word segmentation [30,31,32,33]. Bilingual setups for word segmentation were discussed by [34,35,36,37], but applied to speech transcripts (true phones). Among the most relevant to our approach are the works of [24] on speech-to-translation alignment using attentional Neural Machine Translation (NMT) and of [31,32] for language documentation.…”

Section: Related Workmentioning

confidence: 99%

A Small Griko-Italian Speech Translation Corpus

Boito¹,

Anastasopoulos²,

Villavicencio³

et al. 2018

6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)

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This paper presents an extension to a very low-resource parallel corpus collected in an endangered language, Griko, making it useful for computational research. The corpus consists of 330 utterances (about 20 minutes of speech) which have been transcribed and translated in Italian, with annotations for word-level speech-to-transcription and speech-to-translation alignments. The corpus also includes morphosyntactic tags and word-level glosses. Applying an automatic unit discovery method, pseudo-phones were also generated. We detail how the corpus was collected, cleaned and processed, and we illustrate its use on zero-resource tasks by presenting some baseline results for the task of speech-to-translation alignment and unsupervised word discovery. The dataset is available online, aiming to encourage replicability and diversity in computational language documentation experiments.

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“…[5,7] have investigated such an approach. Changes have been proposed to SMT modeling methods [8,9] to specifically deal with phoneme strings in the target language.…”

Section: Relation To Prior Workmentioning

confidence: 99%

Bootstrapping Text-to-Speech for speech processing in languages without an orthography

Sitaram

Palkar

Chen

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Speech synthesis technology has reached the stage where given a well-designed corpus of audio and accurate transcription an at least understandable synthesizer can be built without necessarily resorting to new innovations. However many languages do not have a welldefined writing system but such languages could still greatly benefit from speech systems. In this paper we consider the case where we have a (potentially large) single speaker database but have no transcriptions and no standardized way to write transcriptions. To address this scenario we propose a method that allows us to bootstrap synthetic voices purely from speech data. We use a novel combination of automatic speech recognition and automatic word segmentation for the bootstrapping. Our experimental results on speech corpora in two languages, English and German, show that synthetic voices that are built using this method are close to understandable. Our method is language-independent and can thus be used to build synthetic voices from a speech corpus in any new language.

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Word segmentation through cross-lingual word-to-phoneme alignment

Cited by 17 publications

References 14 publications

Unsupervised Word Segmentation from Speech with Attention

Unsupervised Word Segmentation from Speech with Attention

A Small Griko-Italian Speech Translation Corpus

Bootstrapping Text-to-Speech for speech processing in languages without an orthography

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