Frontend Token Enhancement for Token-Based Speech Recognition

• URL: http://arxiv.org/abs/2602.04217v1
• Date: Wed, 04 Feb 2026 05:02:15 GMT
• Title: Frontend Token Enhancement for Token-Based Speech Recognition
• Authors: Takanori Ashihara, Shota Horiguchi, Kohei Matsuura, Tsubasa Ochiai, Marc Delcroix,
• Abstract summary: Discretized representations of speech signals are efficient alternatives to continuous features for speech recognition applications.<n>In this work, we introduce a system that estimates clean speech tokens from noisy speech and evaluate it on an ASR backend using semantic tokens.<n>We consider four types of enhancement models based on their input/token domains: wave-to-wave, token-to-output, continuous SSL features-to-token, and wave-to-token.
• Score: 50.35062963870211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discretized representations of speech signals are efficient alternatives to continuous features for various speech applications, including automatic speech recognition (ASR) and speech language models. However, these representations, such as semantic or phonetic tokens derived from clustering outputs of self-supervised learning (SSL) speech models, are susceptible to environmental noise, which can degrade backend task performance. In this work, we introduce a frontend system that estimates clean speech tokens from noisy speech and evaluate it on an ASR backend using semantic tokens. We consider four types of enhancement models based on their input/output domains: wave-to-wave, token-to-token, continuous SSL features-to-token, and wave-to-token. These models are trained independently of ASR backends. Experiments on the CHiME-4 dataset demonstrate that wave-to-token enhancement achieves the best performance among the frontends. Moreover, it mostly outperforms the ASR system based on continuous SSL features.

Related papers

• LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenization [8.365515332927444]
  Recent speech tokenization approaches aim to isolate semantic information from low-level acoustics to better align with language models.<n>We propose LM-SPT, a speech tokenization method that introduces a novel semantic distillation.<n>We show that LM-SPT achieves superior reconstruction fidelity compared to baselines.
  arXiv  Detail & Related papers  (2025-06-20T04:15:14Z)
• A Variational Framework for Improving Naturalness in Generative Spoken Language Models [52.673912922590866]
  We propose an end-to-end variational approach that automatically learns to encode continuous speech attributes to enhance semantic tokens.<n>Our approach eliminates the need for manual extraction and selection of paralinguistic features.<n>It produces preferred speech continuations according to human raters.
  arXiv  Detail & Related papers  (2025-06-17T17:58:17Z)
• DC-Spin: A Speaker-invariant Speech Tokenizer for Spoken Language Models [45.791472119671916]
  Spoken language models (SLMs) process text and speech, enabling simultaneous speech understanding and generation. DC-Spin aims to improve speech tokenization by bridging audio signals and SLM tokens. We propose a chunk-wise approach to enable streamable DC-Spin without retraining and degradation.
  arXiv  Detail & Related papers  (2024-10-31T17:43:13Z)
• dMel: Speech Tokenization made Simple [16.679015298503593]
  We introduce a novel speech representation (dmel) that discretizes mel-filterbank channels into intensity bins.<n>Our approach demonstrates superior performance in preserving audio content, robustness to out-of-domain data, and offers a training-free, natural, and streamable representation.
  arXiv  Detail & Related papers  (2024-07-22T17:51:53Z)
• CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens [49.569695524535454]
  We propose to represent speech with supervised semantic tokens, which are derived from a multilingual speech recognition model by inserting vector quantization into the encoder. Based on the tokens, we further propose a scalable zero-shot TTS synthesizer, CosyVoice, which consists of an LLM for text-to-token generation and a conditional flow matching model for token-to-speech synthesis.
  arXiv  Detail & Related papers  (2024-07-07T15:16:19Z)
• SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models [58.996653700982556]
  Existing speech tokens are not specifically designed for speech language modeling. We propose SpeechTokenizer, a unified speech tokenizer for speech large language models. Experiments show that SpeechTokenizer performs comparably to EnCodec in speech reconstruction and demonstrates strong performance on the SLMTokBench benchmark.
  arXiv  Detail & Related papers  (2023-08-31T12:53:09Z)
• TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition [51.565319173790314]
  TokenSplit is a sequence-to-sequence encoder-decoder model that uses the Transformer architecture. We show that our model achieves excellent performance in terms of separation, both with or without transcript conditioning. We also measure the automatic speech recognition (ASR) performance and provide audio samples of speech synthesis to demonstrate the additional utility of our model.
  arXiv  Detail & Related papers  (2023-08-21T01:52:01Z)
• Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation model [13.572330725278066]
  A novel point of the proposed method is the direct use of the SSL model to obtain embedding vectors from speech representations trained with a large amount of data. The disentangled embeddings will enable us to achieve better reproduction performance for unseen speakers and rhythm transfer conditioned by different speeches.
  arXiv  Detail & Related papers  (2023-04-24T10:15:58Z)
• Fast End-to-End Speech Recognition via a Non-Autoregressive Model and Cross-Modal Knowledge Transferring from BERT [72.93855288283059]
  We propose a non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once) The model consists of an encoder, a decoder, and a position dependent summarizer (PDS)
  arXiv  Detail & Related papers  (2021-02-15T15:18:59Z)

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.