Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR

• URL: http://arxiv.org/abs/2111.06799v1
• Date: Fri, 12 Nov 2021 16:16:46 GMT
• Title: Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR
• Authors: Ondrej Klejch, Electra Wallington, Peter Bell
• Abstract summary: We present a method for cross-lingual training an ASR system using absolutely no transcribed training data from the target language. Our approach uses a novel application of a decipherment algorithm, which operates given only unpaired speech and text data from the target language.
• Score: 13.726142328715897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a method for cross-lingual training an ASR system using absolutely no transcribed training data from the target language, and with no phonetic knowledge of the language in question. Our approach uses a novel application of a decipherment algorithm, which operates given only unpaired speech and text data from the target language. We apply this decipherment to phone sequences generated by a universal phone recogniser trained on out-of-language speech corpora, which we follow with flat-start semi-supervised training to obtain an acoustic model for the new language. To the best of our knowledge, this is the first practical approach to zero-resource cross-lingual ASR which does not rely on any hand-crafted phonetic information. We carry out experiments on read speech from the GlobalPhone corpus, and show that it is possible to learn a decipherment model on just 20 minutes of data from the target language. When used to generate pseudo-labels for semi-supervised training, we obtain WERs that range from 25% to just 5% absolute worse than the equivalent fully supervised models trained on the same data.

Related papers

• TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer [3.9981390090442694]
  We present a novel approach for text independent phone-to-audio alignment based on phoneme recognition, representation learning and knowledge transfer. We evaluate our model using synthetic native data from the TIMIT dataset and the SCRIBE dataset for American and British English. Our proposed model outperforms the state-of-the-art (charsiu) in statistical metrics and has applications in language learning and speech processing systems.
  arXiv  Detail & Related papers  (2024-05-03T14:25:21Z)
• From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition [50.93943755401025]
  We propose a new parameter-efficient learning framework based on neural model reprogramming for cross-lingual speech recognition. We design different auxiliary neural architectures focusing on learnable pre-trained feature enhancement. Our methods outperform existing ASR tuning architectures and their extension with self-supervised losses.
  arXiv  Detail & Related papers  (2023-01-19T02:37:56Z)
• Low-Resource Multilingual and Zero-Shot Multispeaker TTS [25.707717591185386]
  We show that it is possible for a system to learn speaking a new language using just 5 minutes of training data. We show the success of our proposed approach in terms of intelligibility, naturalness and similarity to target speaker.
  arXiv  Detail & Related papers  (2022-10-21T20:03:37Z)
• Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge [0.0]
  The paper is based on our submission to the Oriental Language Recognition 2021 Challenge. For the constrained track, we first trained a Conformer-based encoder-decoder model for multilingual automatic speech recognition. For the unconstrained task, we relied on both externally available pretrained models as well as external data.
  arXiv  Detail & Related papers  (2022-05-14T15:17:08Z)
• Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition [71.49308685090324]
  This paper investigates the influence of different factors (i.e., model architecture, phonotactic model, type of speech representation) on phone recognition in an unknown language. We find that unique sounds, similar sounds, and tone languages remain a major challenge for phonetic inventory discovery.
  arXiv  Detail & Related papers  (2022-01-26T22:12:55Z)
• Simple and Effective Zero-shot Cross-lingual Phoneme Recognition [46.76787843369816]
  This paper extends previous work on zero-shot cross-lingual transfer learning by fine-tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen languages. Experiments show that this simple method significantly outperforms prior work which introduced task-specific architectures.
  arXiv  Detail & Related papers  (2021-09-23T22:50:32Z)
• Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages [51.0542215642794]
  We propose a novel acoustics based intent recognition system that uses discovered phonetic units for intent classification. We present results for two languages families - Indic languages and Romance languages, for two different intent recognition tasks.
  arXiv  Detail & Related papers  (2020-11-07T00:35:31Z)
• Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation [63.16500026845157]
  We introduce speech-to-text translation as an auxiliary task to incorporate additional knowledge of the target language. We show that training ST with human translations is not necessary. Even with pseudo-labels from low-resource MT (200K examples), ST-enhanced transfer brings up to 8.9% WER reduction to direct transfer.
  arXiv  Detail & Related papers  (2020-06-09T19:34:11Z)
• That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages [72.9927937955371]
  We use the resources existing in other languages to train a multilingual automatic speech recognition model. We observe significant improvements across all languages in the multilingual setting, and stark degradation in the crosslingual setting. Our analysis uncovered that even the phones that are unique to a single language can benefit greatly from adding training data from other languages.
  arXiv  Detail & Related papers  (2020-05-16T22:28:09Z)
• Towards Zero-shot Learning for Automatic Phonemic Transcription [82.9910512414173]
  A more challenging problem is to build phonemic transcribers for languages with zero training data. Our model is able to recognize unseen phonemes in the target language without any training data. It achieves 7.7% better phoneme error rate on average over a standard multilingual model.
  arXiv  Detail & Related papers  (2020-02-26T20:38:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.