Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic Data Formats

• URL: http://arxiv.org/abs/2405.04271v1
• Date: Tue, 7 May 2024 12:40:59 GMT
• Title: Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic Data Formats
• Authors: Arne Rubehn, Jessica Nieder, Robert Forkel, Johann-Mattis List,
• Abstract summary: We propose a new approach that can create binary feature dynamically for all sounds that can be represented in the the standardized version of the International Phonetic Alphabet proposed by the Cross-Linguistic Transcription Systems (CLTS) reference catalog. Our system is not only useful to provide a straightforward means to compare the similarity of speech sounds, but also illustrates its potential to be used in future cross-linguistic machine learning applications.
• Score: 1.087459729391301
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When comparing speech sounds across languages, scholars often make use of feature representations of individual sounds in order to determine fine-grained sound similarities. Although binary feature systems for large numbers of speech sounds have been proposed, large-scale computational applications often face the challenges that the proposed feature systems -- even if they list features for several thousand sounds -- only cover a smaller part of the numerous speech sounds reflected in actual cross-linguistic data. In order to address the problem of missing data for attested speech sounds, we propose a new approach that can create binary feature vectors dynamically for all sounds that can be represented in the the standardized version of the International Phonetic Alphabet proposed by the Cross-Linguistic Transcription Systems (CLTS) reference catalog. Since CLTS is actively used in large data collections, covering more than 2,000 distinct language varieties, our procedure for the generation of binary feature vectors provides immediate access to a very large collection of multilingual wordlists. Testing our feature system in different ways on different datasets proves that the system is not only useful to provide a straightforward means to compare the similarity of speech sounds, but also illustrates its potential to be used in future cross-linguistic machine learning applications.

Related papers

• Language Modelling for Speaker Diarization in Telephonic Interviews [13.851959980488529]
  Combination of acoustic features and linguistic content shows a 84.29% improvement in terms of a word-level DER. The results of this study confirms that linguistic content can be efficiently used for some speaker recognition tasks.
  arXiv  Detail & Related papers  (2025-01-28T18:18:04Z)
• Cascaded Cross-Modal Transformer for Audio-Textual Classification [30.643750999989233]
  We propose to harness the inherent value of multimodal representations by transcribing speech using automatic speech recognition (ASR) models. We thus obtain an audio-textual (multimodal) representation for each data sample. We were declared the winning solution in the Requests Sub-Challenge of the ACM Multimedia 2023 Computational Paralinguistics Challenge.
  arXiv  Detail & Related papers  (2024-01-15T10:18:08Z)
• ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition [100.30565531246165]
  Speech recognition systems require dataset-specific tuning. This tuning requirement can lead to systems failing to generalise to other datasets and domains. We introduce the End-to-end Speech Benchmark (ESB) for evaluating the performance of a single automatic speech recognition system.
  arXiv  Detail & Related papers  (2022-10-24T15:58:48Z)
• Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition [62.997667081978825]
  Code-Switching (CS) is referred to the phenomenon of alternately using words and phrases from different languages. We propose a simple yet effective data augmentation in which audio and corresponding labels of different source languages are transcribed. We show that this augmentation can even improve the model's performance on inter-sentential language switches not seen during training by 5,03% WER.
  arXiv  Detail & Related papers  (2022-10-17T12:15:57Z)
• Cross-lingual Low Resource Speaker Adaptation Using Phonological Features [2.8080708404213373]
  We train a language-agnostic multispeaker model conditioned on a set of phonologically derived features common across different languages. With as few as 32 and 8 utterances of target speaker data, we obtain high speaker similarity scores and naturalness comparable to the corresponding literature.
  arXiv  Detail & Related papers  (2021-11-17T12:33:42Z)
• Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity [81.51206991542242]
  Cross-lingual transfer offers a compelling way to help bridge this digital divide. Current cross-lingual algorithms have shown success in text-based tasks and speech-related tasks over some low-resource languages. We propose a language similarity approach that can efficiently identify acoustic cross-lingual transfer pairs across hundreds of languages.
  arXiv  Detail & Related papers  (2021-11-02T01:55:17Z)
• Improve Cross-lingual Voice Cloning Using Low-quality Code-switched Data [11.18504333789534]
  We propose to use low-quality code-switched found data from the non-target speakers to achieve cross-lingual voice cloning for the target speakers. Experiments show that our proposed method can generate high-quality code-switched speech in the target voices in terms of both naturalness and speaker consistency.
  arXiv  Detail & Related papers  (2021-10-14T08:16:06Z)
• 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)
• Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features [41.951988293049205]
  We propose a two-stage approach, which comprises unsupervised acoustic modeling and decoding, followed by pattern mining in acoustic unit sequences. The proposed system is able to effectively extract topic-related words and phrases from the lecture recordings on MIT OpenCourseWare.
  arXiv  Detail & Related papers  (2020-11-03T20:06:48Z)
• Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation [125.59372403631006]
  We propose a semi-supervised learning approach for multi-speaker text-to-speech (TTS) A multi-speaker TTS model can learn from the untranscribed audio via the proposed encoder-decoder framework with discrete speech representation. We found the model can benefit from the proposed semi-supervised learning approach even when part of the unpaired speech data is noisy.
  arXiv  Detail & Related papers  (2020-05-16T15:47:11Z)
• Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text [69.55642178336953]
  We present an approach to unsupervised audio representation learning. Based on a triplet neural network architecture, we harnesses semantically related cross-modal information to estimate audio track-relatedness. We show that our approach is invariant to the variety of annotation styles as well as to the different languages of this collection.
  arXiv  Detail & Related papers  (2020-03-27T07:37:15Z)

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.