Smart Speech Segmentation using Acousto-Linguistic Features with
look-ahead
- URL: http://arxiv.org/abs/2210.14446v2
- Date: Thu, 27 Oct 2022 05:38:58 GMT
- Title: Smart Speech Segmentation using Acousto-Linguistic Features with
look-ahead
- Authors: Piyush Behre, Naveen Parihar, Sharman Tan, Amy Shah, Eva Sharma,
Geoffrey Liu, Shuangyu Chang, Hosam Khalil, Chris Basoglu, Sayan Pathak
- Abstract summary: We present a hybrid approach that leverages both acoustic and language information to improve segmentation.
On average, our models improve segmentation-F0.5 score by 9.8% over baseline.
For the downstream task of machine translation, it improves the translation BLEU score by an average of 1.05 points.
- Score: 3.579111205766969
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Segmentation for continuous Automatic Speech Recognition (ASR) has
traditionally used silence timeouts or voice activity detectors (VADs), which
are both limited to acoustic features. This segmentation is often overly
aggressive, given that people naturally pause to think as they speak.
Consequently, segmentation happens mid-sentence, hindering both punctuation and
downstream tasks like machine translation for which high-quality segmentation
is critical. Model-based segmentation methods that leverage acoustic features
are powerful, but without an understanding of the language itself, these
approaches are limited. We present a hybrid approach that leverages both
acoustic and language information to improve segmentation. Furthermore, we show
that including one word as a look-ahead boosts segmentation quality. On
average, our models improve segmentation-F0.5 score by 9.8% over baseline. We
show that this approach works for multiple languages. For the downstream task
of machine translation, it improves the translation BLEU score by an average of
1.05 points.
Related papers
- Multilingual Audio-Visual Speech Recognition with Hybrid CTC/RNN-T Fast Conformer [59.57249127943914]
We present a multilingual Audio-Visual Speech Recognition model incorporating several enhancements to improve performance and audio noise robustness.
We increase the amount of audio-visual training data for six distinct languages, generating automatic transcriptions of unlabelled multilingual datasets.
Our proposed model achieves new state-of-the-art performance on the LRS3 dataset, reaching WER of 0.8%.
arXiv Detail & Related papers (2024-03-14T01:16:32Z) - Revisiting speech segmentation and lexicon learning with better features [29.268728666438495]
We revisit a self-supervised method that segments unlabelled speech into word-like segments.
We start from the two-stage duration-penalised dynamic programming method.
In the first acoustic unit discovery stage, we replace contrastive predictive coding features with HuBERT.
After word segmentation in the second stage, we get an acoustic word embedding for each segment by averaging HuBERT features.
arXiv Detail & Related papers (2024-01-31T15:06:34Z) - Long-Form End-to-End Speech Translation via Latent Alignment
Segmentation [6.153530338207679]
Current simultaneous speech translation models can process audio only up to a few seconds long.
We propose a novel segmentation approach for a low-latency end-to-end speech translation.
We show that the proposed approach achieves state-of-the-art quality at no additional computational cost.
arXiv Detail & Related papers (2023-09-20T15:10:12Z) - SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural
Machine Translation [51.881877192924414]
Sub-word segmentation is an essential pre-processing step for Neural Machine Translation (NMT)
This paper introduces SelfSeg, a self-supervised neural sub-word segmentation method.
SelfSeg is much faster to train/decode and requires only monolingual dictionaries instead of parallel corpora.
arXiv Detail & Related papers (2023-07-31T04:38:47Z) - Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic
Sentence Segmentation [65.6736056006381]
We present a multilingual punctuation-agnostic sentence segmentation method covering 85 languages.
Our method outperforms all the prior best sentence-segmentation tools by an average of 6.1% F1 points.
By using our method to match sentence segmentation to the segmentation used during training of MT models, we achieve an average improvement of 2.3 BLEU points.
arXiv Detail & Related papers (2023-05-30T09:49:42Z) - Learning Context-aware Classifier for Semantic Segmentation [88.88198210948426]
In this paper, contextual hints are exploited via learning a context-aware classifier.
Our method is model-agnostic and can be easily applied to generic segmentation models.
With only negligible additional parameters and +2% inference time, decent performance gain has been achieved on both small and large models.
arXiv Detail & Related papers (2023-03-21T07:00:35Z) - Semantics-Aware Dynamic Localization and Refinement for Referring Image
Segmentation [102.25240608024063]
Referring image segments an image from a language expression.
We develop an algorithm that shifts from being localization-centric to segmentation-language.
Compared to its counterparts, our method is more versatile yet effective.
arXiv Detail & Related papers (2023-03-11T08:42:40Z) - Speech Segmentation Optimization using Segmented Bilingual Speech Corpus
for End-to-end Speech Translation [16.630616128169372]
We propose a speech segmentation method using a binary classification model trained using a segmented bilingual speech corpus.
Experimental results revealed that the proposed method is more suitable for cascade and end-to-end ST systems than conventional segmentation methods.
arXiv Detail & Related papers (2022-03-29T12:26:56Z) - SHAS: Approaching optimal Segmentation for End-to-End Speech Translation [0.0]
Speech translation models are unable to directly process long audios, like TED talks, which have to be split into shorter segments.
We propose Supervised Hybrid Audio (SHAS), a method that can effectively learn the optimal segmentation from any manually segmented speech corpus.
Experiments on MuST-C and mTEDx show that SHAS retains 95-98% of the manual segmentation's BLEU score, compared to the 87-93% of the best existing methods.
arXiv Detail & Related papers (2022-02-09T23:55:25Z) - FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and
Fusing Fine-Grained Voice Fragments With Attention [66.77490220410249]
We propose FragmentVC, in which the latent phonetic structure of the utterance from the source speaker is obtained from Wav2Vec 2.0.
FragmentVC is able to extract fine-grained voice fragments from the target speaker utterance(s) and fuse them into the desired utterance.
This approach is trained with reconstruction loss only without any disentanglement considerations between content and speaker information.
arXiv Detail & Related papers (2020-10-27T09:21:03Z) - Contextualized Translation of Automatically Segmented Speech [20.334746967390164]
We train our models on randomly segmented data and compare two approaches: fine-tuning and adding the previous segment as context.
Our solution is more robust to VAD-segmented input, outperforming a strong base model and the fine-tuning on different VAD segmentations of an English-German test set by up to 4.25 BLEU points.
arXiv Detail & Related papers (2020-08-05T17:52:25Z)
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.