Improving Automatic Speech Recognition in Noisy Environments: A Case Study on Cocktail Party Speech Recognition

核心概念

This paper presents an end-to-end model that combines a speech enhancement module (ConVoiFilter) and an automatic speech recognition (ASR) module to improve speech recognition performance in noisy, crowded environments. The model utilizes a single-channel speech enhancement approach to isolate the target speaker's voice from background noise and then feeds the enhanced audio into the ASR module.

要約

The paper presents an end-to-end model for improving automatic speech recognition (ASR) in noisy, crowded environments, such as cocktail party settings. The model consists of two main components:

1. Target speaker's voice enhancement (ConVoiFilter):

   • This module aims to remove all noise and interfering speech from the noisy audio input, producing a clean utterance for the target speaker.
   • It uses a speaker encoder module to extract an embedding vector that identifies the target speaker, and then performs cross-extraction of the speaker embedding from the noisy audio.
   • The module then uses a series of conformer blocks to estimate a mask that is applied to the magnitude spectrogram of the noisy audio, effectively enhancing the target speaker's voice.
   • The enhanced audio is reconstructed using the estimated mask and the original phase information.

2. Automatic speech recognition (ASR):

   • The ASR module uses a pre-trained wav2vec2 model as the speech encoder, followed by an RNN transducer as the decoder.
   • To address potential noise artifacts in the enhanced audio, the wav2vec2 model is pre-trained on data augmented with noise and room reverb.

The authors also propose a joint fine-tuning strategy to optimize the ConVoiFilter and ASR modules together, addressing the challenges of connecting the high-resolution enhancement module to the lower-resolution ASR module.

The model is evaluated on various types of data, including clean audio, noisy audio with and without cross-talk, and audio with only ambient noise or reverberation. The results show that the end-to-end ConVoiFilter-ASR model significantly outperforms the ASR-only models, especially in the presence of cross-talk, achieving a word error rate (WER) of 14.51% compared to 75.19% for the ASR-only model.

The authors also conduct an ablation study to demonstrate the effectiveness of the key components of the ConVoiFilter model, such as the use of the x-vector speaker encoder, the conformer-based mask estimation, and the SI-SNR loss function.

統計

In the "Noisy Audio" column, the ASR noisy model achieves a WER of 84.14%, while the end-to-end ConVoiFilter-ASR noisy model reduces the WER to 25.14%.
In the "Cross-talk" column, the ASR based model has a WER of 50.72%, while the end-to-end ConVoiFilter-ASR noisy model reduces the WER to 13.23%.
On the LibriCSS dataset, the ConVoiFilter + Whisper-large model achieves a WER of 16.83% in the 40% overlap ratio, compared to 32.73% for the Whisper-large model alone.

引用

"Our system was evaluated using five different types of data. The first is the 'Clean Audio' which consists of the original audio. The second type is the 'Noisy Audio' which is the output of the data processing pipeline (depicted in Figure 2) applied to the clean audio."
"Generally, the end-to-end models perform better than other models across most input sets, except for clean audio."
"The results clearly indicate that ConVoiFilter significantly improves Whisper, particularly in reducing WER in overlapping audio."

抽出されたキーインサイト

Convoifilter

by Thai-Binh Ng... 場所 arxiv.org 04-09-2024

https://arxiv.org/pdf/2308.11380.pdf

深掘り質問

How can the ConVoiFilter model be further improved to handle more complex acoustic environments, such as those with multiple target speakers or highly dynamic noise sources?

To enhance the ConVoiFilter model for more intricate acoustic scenarios, several strategies can be implemented. Firstly, incorporating multi-speaker separation techniques using advanced speaker diarization algorithms can help identify and isolate multiple target speakers within a noisy environment. By enhancing the speaker extraction module to handle speaker overlap and dynamic speaker changes, the model can effectively separate and enhance the voices of different speakers simultaneously. Additionally, integrating adaptive noise estimation and suppression mechanisms can enable the model to dynamically adjust to varying noise levels and types, ensuring robust performance in highly dynamic noise environments. Employing advanced signal processing techniques, such as adaptive filtering and beamforming, can further improve the model's ability to extract target speaker voices amidst complex acoustic backgrounds.

What are the potential applications of this technology beyond speech recognition, such as in teleconferencing or human-robot interaction?

The technology showcased in the ConVoiFilter model has diverse applications beyond speech recognition, particularly in teleconferencing and human-robot interaction scenarios. In teleconferencing, the model can be utilized to enhance the audio quality by isolating and enhancing the voices of individual speakers, leading to clearer and more intelligible communication during conference calls. This can significantly improve the overall user experience and comprehension in remote communication settings. Moreover, in human-robot interaction, the model can enable robots to better understand and respond to human commands in noisy environments, enhancing the efficiency and accuracy of human-robot communication. By integrating the ConVoiFilter technology into teleconferencing systems and robotic devices, seamless and effective communication can be achieved in various real-world applications.

How can the joint fine-tuning strategy be extended to incorporate additional modalities, such as visual cues, to further enhance the speech separation and recognition performance?

Expanding the joint fine-tuning strategy to incorporate additional modalities, such as visual cues, can significantly enhance speech separation and recognition performance. By integrating visual information, such as lip movements or facial expressions, with the audio signals processed by the ConVoiFilter model, a multimodal approach can be adopted to improve speaker separation and recognition accuracy. This integration can be achieved by developing a fusion model that combines audio and visual features, allowing the system to leverage both modalities for enhanced speech processing. Through joint optimization of the audio-visual model, the system can effectively utilize complementary information from visual cues to refine speaker extraction and enhance speech recognition in challenging acoustic environments. This multimodal approach holds great potential for advancing the capabilities of the ConVoiFilter model in real-world applications where audio-visual integration is crucial for accurate and robust speech processing.