Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method

• URL: http://arxiv.org/abs/2309.08626v1
• Date: Tue, 12 Sep 2023 06:05:57 GMT
• Title: Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method
• Authors: Juntae Kim, Minkyu Lim, and Seokjin Hong
• Abstract summary: Inverse text normalization (ITN) is crucial for converting spoken-form into written-form, especially in the context of automatic speech recognition (ASR) We propose a direct training approach that utilizes ASR-generated written or spoken text, with pairs augmented through ASR linguistic context emulation and a semi-supervised learning method enhanced by a large language model. Our proposed methods remarkably improved ITN performance in various ASR scenarios.
• Score: 4.343606621506086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inverse text normalization (ITN) is crucial for converting spoken-form into written-form, especially in the context of automatic speech recognition (ASR). While most downstream tasks of ASR rely on written-form, ASR systems often output spoken-form, highlighting the necessity for robust ITN in product-level ASR-based applications. Although neural ITN methods have shown promise, they still encounter performance challenges, particularly when dealing with ASR-generated spoken text. These challenges arise from the out-of-domain problem between training data and ASR-generated text. To address this, we propose a direct training approach that utilizes ASR-generated written or spoken text, with pairs augmented through ASR linguistic context emulation and a semi-supervised learning method enhanced by a large language model, respectively. Additionally, we introduce a post-aligning method to manage unpredictable errors, thereby enhancing the reliability of ITN. Our experiments show that our proposed methods remarkably improved ITN performance in various ASR scenarios.

Related papers

• Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks [68.79880423713597]
  We introduce a method that utilizes the ASR system's lattice output instead of relying solely on the top hypothesis. Our in-context learning experiments, covering spoken question answering and intent classification, underline the LLM's resilience to noisy speech transcripts.
  arXiv  Detail & Related papers  (2024-01-05T17:58:10Z)
• Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments [49.38965743465124]
  This paper introduces a streaming Transformer-Transducer that jointly generates automatic speech recognition (ASR) and speech translation (ST) outputs using a single decoder. Experiments in monolingual and multilingual settings demonstrate that our approach achieves the best quality-latency balance.
  arXiv  Detail & Related papers  (2023-07-07T02:26:18Z)
• Improving Data Driven Inverse Text Normalization using Data Augmentation [14.820077884045645]
  Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form. We present a data augmentation technique that effectively generates rich spoken-written numeric pairs from out-of-domain textual data. We empirically demonstrate that ITN model trained using our data augmentation technique consistently outperform ITN model trained using only in-domain data.
  arXiv  Detail & Related papers  (2022-07-20T06:07:26Z)
• 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)
• Cross-sentence Neural Language Models for Conversational Speech Recognition [17.317583079824423]
  We propose an effective cross-sentence neural LM approach that reranks the ASR N-best hypotheses of an upcoming sentence. We also explore to extract task-specific global topical information of the cross-sentence history.
  arXiv  Detail & Related papers  (2021-06-13T05:30:16Z)
• N-Best ASR Transformer: Enhancing SLU Performance using Multiple ASR Hypotheses [0.0]
  Spoken Language Understanding (SLU) parses speech into semantic structures like dialog acts and slots. We show that our approach significantly outperforms the prior state-of-the-art when subjected to the low data regime.
  arXiv  Detail & Related papers  (2021-06-11T17:29:00Z)
• An Approach to Improve Robustness of NLP Systems against ASR Errors [39.57253455717825]
  Speech-enabled systems typically first convert audio to text through an automatic speech recognition model and then feed the text to downstream natural language processing modules. The errors of the ASR system can seriously downgrade the performance of the NLP modules. Previous work has shown it is effective to employ data augmentation methods to solve this problem by injecting ASR noise during the training process.
  arXiv  Detail & Related papers  (2021-03-25T05:15:43Z)
• Hallucination of speech recognition errors with sequence to sequence learning [16.39332236910586]
  When plain text data is to be used to train systems for spoken language understanding or ASR, a proven strategy is to hallucinate what the ASR outputs would be given a gold transcription. We present novel end-to-end models to directly predict hallucinated ASR word sequence outputs, conditioning on an input word sequence as well as a corresponding phoneme sequence. This improves prior published results for recall of errors from an in-domain ASR system's transcription of unseen data, as well as an out-of-domain ASR system's transcriptions of audio from an unrelated task.
  arXiv  Detail & Related papers  (2021-03-23T02:09:39Z)
• Pretraining Techniques for Sequence-to-Sequence Voice Conversion [57.65753150356411]
  Sequence-to-sequence (seq2seq) voice conversion (VC) models are attractive owing to their ability to convert prosody. We propose to transfer knowledge from other speech processing tasks where large-scale corpora are easily available, typically text-to-speech (TTS) and automatic speech recognition (ASR) We argue that VC models with such pretrained ASR or TTS model parameters can generate effective hidden representations for high-fidelity, highly intelligible converted speech.
  arXiv  Detail & Related papers  (2020-08-07T11:02:07Z)
• Improving Readability for Automatic Speech Recognition Transcription [50.86019112545596]
  We propose a novel NLP task called ASR post-processing for readability (APR) APR aims to transform the noisy ASR output into a readable text for humans and downstream tasks while maintaining the semantic meaning of the speaker. We compare fine-tuned models based on several open-sourced and adapted pre-trained models with the traditional pipeline method.
  arXiv  Detail & Related papers  (2020-04-09T09:26:42Z)
• Joint Contextual Modeling for ASR Correction and Language Understanding [60.230013453699975]
  We propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with language understanding (LU) We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.
  arXiv  Detail & Related papers  (2020-01-28T22:09:25Z)

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.