Topic Identification For Spontaneous Speech: Enriching Audio Features With Embedded Linguistic Information

• URL: http://arxiv.org/abs/2307.11450v1
• Date: Fri, 21 Jul 2023 09:30:46 GMT
• Title: Topic Identification For Spontaneous Speech: Enriching Audio Features With Embedded Linguistic Information
• Authors: Dejan Porjazovski, Tam\'as Gr\'osz, Mikko Kurimo
• Abstract summary: Traditional topic identification solutions from audio rely on an automatic speech recognition system (ASR) to produce transcripts. We compare audio-only and hybrid techniques of jointly utilising text and audio features. The models evaluated on spontaneous Finnish speech demonstrate that purely audio-based solutions are a viable option when ASR components are not available.
• Score: 10.698093106994804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional topic identification solutions from audio rely on an automatic speech recognition system (ASR) to produce transcripts used as input to a text-based model. These approaches work well in high-resource scenarios, where there are sufficient data to train both components of the pipeline. However, in low-resource situations, the ASR system, even if available, produces low-quality transcripts, leading to a bad text-based classifier. Moreover, spontaneous speech containing hesitations can further degrade the performance of the ASR model. In this paper, we investigate alternatives to the standard text-only solutions by comparing audio-only and hybrid techniques of jointly utilising text and audio features. The models evaluated on spontaneous Finnish speech demonstrate that purely audio-based solutions are a viable option when ASR components are not available, while the hybrid multi-modal solutions achieve the best results.

Related papers

• Style-Talker: Finetuning Audio Language Model and Style-Based Text-to-Speech Model for Fast Spoken Dialogue Generation [16.724603503894166]
  Style-Talker is an innovative framework that fine-tunes an audio LLM alongside a style-based TTS model for fast spoken dialog generation. Our experimental results show that Style-Talker significantly outperforms the conventional cascade and speech-to-speech baselines in terms of both dialogue naturalness and coherence.
  arXiv  Detail & Related papers  (2024-08-13T04:35:11Z)
• Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models [21.85677682584916]
  speculative speech recognition (SSR) We propose a model which does SSR by combining a RNN-Transducer-based ASR system with an audio-ed language model (LM)
  arXiv  Detail & Related papers  (2024-07-05T16:52:55Z)
• Learning Speech Representation From Contrastive Token-Acoustic Pretraining [57.08426714676043]
  We propose "Contrastive Token-Acoustic Pretraining (CTAP)", which uses two encoders to bring phoneme and speech into a joint multimodal space. The proposed CTAP model is trained on 210k speech and phoneme pairs, achieving minimally-supervised TTS, VC, and ASR.
  arXiv  Detail & Related papers  (2023-09-01T12:35:43Z)
• Deliberation Model for On-Device Spoken Language Understanding [69.5587671262691]
  We propose a novel deliberation-based approach to end-to-end (E2E) spoken language understanding (SLU) We show that our approach can significantly reduce the degradation when moving from natural speech to synthetic speech training.
  arXiv  Detail & Related papers  (2022-04-04T23:48:01Z)
• Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings [53.11450530896623]
  This paper presents a streaming speaker-attributed automatic speech recognition (SA-ASR) model that can recognize "who spoke what" Our model is based on token-level serialized output training (t-SOT) which was recently proposed to transcribe multi-talker speech in a streaming fashion. The proposed model achieves substantially better accuracy than a prior streaming model and shows comparable or sometimes even superior results to the state-of-the-art offline SA-ASR model.
  arXiv  Detail & Related papers  (2022-03-30T21:42:00Z)
• ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion [49.617722668505834]
  We show that our approach permits the application of speech synthesis and voice conversion to improve ASR systems using only one target-language speaker during model training. It is possible to obtain promising ASR training results with our data augmentation method using only a single real speaker in a target language.
  arXiv  Detail & Related papers  (2022-03-29T11:55:30Z)
• Joint Speech Recognition and Audio Captioning [37.205642807313545]
  Speech samples recorded in both indoor and outdoor environments are often contaminated with secondary audio sources. We aim to bring together the growing field of automated audio captioning (AAC) and the thoroughly studied automatic speech recognition (ASR) We propose several approaches for end-to-end joint modeling of ASR and AAC tasks.
  arXiv  Detail & Related papers  (2022-02-03T04:42:43Z)
• Using multiple reference audios and style embedding constraints for speech synthesis [68.62945852651383]
  The proposed model can improve the speech naturalness and content quality with multiple reference audios. The model can also outperform the baseline model in ABX preference tests of style similarity.
  arXiv  Detail & Related papers  (2021-10-09T04:24:29Z)
• On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era [0.0]
  We create transcripts from the original speech by applying three modern ASR systems. For extraction and learning of acoustic speech features, we utilise openSMILE, openXBoW, DeepSpectrum, and auDeep. We achieve state-of-the-art unweighted average recall values of $73.6,%$ and $73.8,%$ on the speaker-independent development and test partitions of IEMOCAP.
  arXiv  Detail & Related papers  (2021-04-20T17:10:01Z)
• Streaming Multi-talker Speech Recognition with Joint Speaker Identification [77.46617674133556]
  SURIT employs the recurrent neural network transducer (RNN-T) as the backbone for both speech recognition and speaker identification. We validate our idea on the Librispeech dataset -- a multi-talker dataset derived from Librispeech, and present encouraging results.
  arXiv  Detail & Related papers  (2021-04-05T18:37:33Z)
• An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering [11.307739925111944]
  In a spoken multiple-choice question answering (SMCQA) task, given a passage, a question, and multiple choices all in the form of speech, the machine needs to pick the correct choice to answer the question. This study concentrates on designing a BERT-based SMCQA framework, which not only inherits the advantages of contextualized language representations learned by BERT, but integrates the complementary acoustic-level information distilled from audio with the text-level information.
  arXiv  Detail & Related papers  (2020-05-25T14:41:28Z)

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.