Anatomy of Industrial Scale Multilingual ASR

• URL: http://arxiv.org/abs/2404.09841v2
• Date: Tue, 16 Apr 2024 14:55:13 GMT
• Title: Anatomy of Industrial Scale Multilingual ASR
• Authors: Francis McCann Ramirez, Luka Chkhetiani, Andrew Ehrenberg, Robert McHardy, Rami Botros, Yash Khare, Andrea Vanzo, Taufiquzzaman Peyash, Gabriel Oexle, Michael Liang, Ilya Sklyar, Enver Fakhan, Ahmed Etefy, Daniel McCrystal, Sam Flamini, Domenic Donato, Takuya Yoshioka,
• Abstract summary: This paper describes AssemblyAI's industrial-scale automatic speech recognition (ASR) system. Our system leverages a diverse training dataset comprising unsupervised (12.5M hours), supervised (188k hours), and pseudo-labeled (1.6M hours) data across four languages.
• Score: 13.491861238522421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes AssemblyAI's industrial-scale automatic speech recognition (ASR) system, designed to meet the requirements of large-scale, multilingual ASR serving various application needs. Our system leverages a diverse training dataset comprising unsupervised (12.5M hours), supervised (188k hours), and pseudo-labeled (1.6M hours) data across four languages. We provide a detailed description of our model architecture, consisting of a full-context 600M-parameter Conformer encoder pre-trained with BEST-RQ and an RNN-T decoder fine-tuned jointly with the encoder. Our extensive evaluation demonstrates competitive word error rates (WERs) against larger and more computationally expensive models, such as Whisper large and Canary-1B. Furthermore, our architectural choices yield several key advantages, including an improved code-switching capability, a 5x inference speedup compared to an optimized Whisper baseline, a 30% reduction in hallucination rate on speech data, and a 90% reduction in ambient noise compared to Whisper, along with significantly improved time-stamp accuracy. Throughout this work, we adopt a system-centric approach to analyzing various aspects of fully-fledged ASR models to gain practically relevant insights useful for real-world services operating at scale.

Related papers

• Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations [16.577870835480585]
  We present a comprehensive analysis on building ASR systems with discrete codes. We investigate different methods for training such as quantization schemes and time-domain vs spectral feature encodings. We introduce a pipeline that outperforms Encodec at similar bit-rate.
  arXiv  Detail & Related papers  (2024-07-03T20:51:41Z)
• Exploring the limits of decoder-only models trained on public speech recognition corpora [36.446905777292066]
  Decoder-Only Transformer for ASR (DOTA) model comprehensively outperforms the encoder-decoder open source replication of Whisper (OWSM) on nearly all English ASR benchmarks and outperforms Whisper large-v3 on 7 out of 15 test sets.
  arXiv  Detail & Related papers  (2024-01-31T23:29:42Z)
• From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition [50.93943755401025]
  We propose a new parameter-efficient learning framework based on neural model reprogramming for cross-lingual speech recognition. We design different auxiliary neural architectures focusing on learnable pre-trained feature enhancement. Our methods outperform existing ASR tuning architectures and their extension with self-supervised losses.
  arXiv  Detail & Related papers  (2023-01-19T02:37:56Z)
• Analyzing And Improving Neural Speaker Embeddings for ASR [54.30093015525726]
  We present our efforts w.r.t integrating neural speaker embeddings into a conformer based hybrid HMM ASR system. Our best Conformer-based hybrid ASR system with speaker embeddings achieves 9.0% WER on Hub5'00 and Hub5'01 with training on SWB 300h.
  arXiv  Detail & Related papers  (2023-01-11T16:56:03Z)
• SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition [49.42625022146008]
  We present the advantages of applying SRU++ in ASR tasks by comparing with Conformer across multiple ASR benchmarks. Specifically, SRU++ can surpass Conformer on long-form speech input with a large margin, based on our analysis.
  arXiv  Detail & Related papers  (2021-10-11T19:23:50Z)
• Improving RNN-T ASR Performance with Date-Time and Location Awareness [6.308539010172309]
  We show that contextual information, when used individually, improves overall performance by as much as 3.48% relative to the baseline. On specific domains, these contextual signals show improvements as high as 11.5%, without any significant degradation on others. Our results indicate that with limited data to train the ASR model, contextual signals can improve the performance significantly.
  arXiv  Detail & Related papers  (2021-06-11T05:57:30Z)
• Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition [45.858039215825656]
  We propose a new encoder that adopts globally attentive locally recurrent (GALR) networks and directly takes raw waveform as input. Experiments are conducted on a benchmark dataset AISHELL-2 and two large-scale Mandarin speech corpus of 5,000 hours and 21,000 hours.
  arXiv  Detail & Related papers  (2021-06-08T12:12:33Z)
• Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers [56.56220390953412]
  We extend our prior work by introducing the Conformer architecture to further improve the accuracy. We demonstrate that the extended Transformer provides state-of-the-art end-to-end ASR performance.
  arXiv  Detail & Related papers  (2021-04-19T16:18:00Z)
• You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation [59.31769998728787]
  We build our TTS system on an ASR training database and then extend the data with synthesized speech to train a recognition model. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for test-other.
  arXiv  Detail & Related papers  (2020-05-14T17:24:57Z)

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.