Summary Harnessing Instruction-Tuned LLM in End-to-End Speech Recognition arxiv.org
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The text describes a method that integrates instruction-tuned large language models (LLMs) into automatic speech recognition (ASR) systems, using a two-stage approach to improve performance, particularly on written-style tasks, by leveraging the LLMs for grammatical error correction.
Slides
Slide Presentation (11 slides)
Key Points
- Leveraging the zero-shot power of instruction-tuned large language models (LLMs) to enhance end-to-end automatic speech recognition (ASR) performance
- Integrating an instruction-tuned LLM, specifically Llama2, into a hybrid connectionist temporal classification (CTC) and attention-based ASR architecture
- Using the LLM as a front-end for the decoder network to capitalize on its potential as a zero-shot grammatical error correction model
- Evaluating the proposed model on various English ASR tasks, including LibriSpeech, TED-LIUM2, and CoVoST2, and demonstrating consistent performance improvements over the baseline hybrid CTC-attention model
- Conducting ablation studies to assess the importance of the LLM integration and the prompt design, and comparing the proposed approach with other methods for integrating LLMs into ASR
Summaries
20 word summary
Integrates instruction-tuned LLMs into end-to-end ASR, leveraging LLMs for grammatical error correction. Two-stage approach improves performance, especially on written-style tasks.
47 word summary
This work integrates instruction-tuned LLMs into end-to-end ASR, leveraging LLMs for grammatical error correction. The two-stage approach trains a baseline ASR model, then a new decoder with the LLM as front-end. Experiments show consistent improvements, especially on written-style tasks, though uncommon words in audiobooks required LLM-based rescoring.
126 word summary
This work explores integrating instruction-tuned large language models (LLMs) into end-to-end automatic speech recognition (ASR). The key idea is to leverage the LLM's potential as a zero-shot grammatical error correction model to enhance ASR performance. The proposed approach involves a two-stage training process, where a baseline ASR model is first trained, and then a new decoder network is trained with the LLM as the front-end. The model was evaluated on various English ASR tasks, showing consistent improvements over the baseline, especially on tasks with written-style text. However, the model struggled with uncommon words in audiobooks, which was mitigated by combining it with LLM-based rescoring. Ablation studies confirmed the effectiveness of the LLM integration and the importance of the prompt in maximizing the LLM's zero-shot learning capability.
331 word summary
This work explores the integration of instruction-tuned large language models (LLMs), specifically Llama2, into an end-to-end automatic speech recognition (ASR) framework. The key idea is to leverage the LLM's potential as a zero-shot grammatical error correction model to enhance ASR performance.
The proposed approach involves a two-stage training process. First, a baseline hybrid connectionist temporal classification (CTC) and attention-based end-to-end ASR model is trained. Then, a new decoder network is trained from scratch, using the pre-trained encoder and CTC networks, with the LLM as the front-end. The objective function is defined by the negative log-likelihood of the joint probability of the target sequence and the ASR hypothesis, with the intractable marginalization over hypotheses approximated through sampling.
During inference, the best ASR hypothesis is obtained using the encoder and CTC decoding, and then joint CTC and attention decoding is performed with the LLM-enhanced decoder.
The proposed model was evaluated on various English ASR tasks, including LibriSpeech (LS), TED-LIUM2 (TED2), and CoVoST2 (CV2), and compared to the baseline hybrid CTC-attention model. The results demonstrate that the integration of the instruction-tuned LLM consistently outperformed the baseline model on tasks other than LS-960, with the most significant gains observed on the CV2 task. This is attributed to the LLM's ability to extract more precise linguistic information from the unnormalized written-style text in CV2.
In the LS-960 task, the proposed model showed limited improvements, which is attributed to the LLM's tendency to struggle with recognizing "uncommon" words, such as character names in audiobooks. This issue was mitigated to some extent by combining the proposed model with LLM-based rescoring.
Ablation studies confirmed the effectiveness of the LLM integration and highlighted the crucial role of the prompt in maximizing the LLM's zero-shot learning capability. The proposed approach was also compared to other methods for integrating LLMs into ASR, demonstrating competitive or superior results.
Overall, this work demonstrates the promising potential of harnessing the versatile linguistic knowledge embedded within instruction-tuned LLMs to advance the field of end-to-end speech recognition.
643 word summary
HARNESSING THE ZERO-SHOT POWER OF INSTRUCTION-TUNED LARGE LANGUAGE MODELS IN END-TO-END SPEECH RECOGNITION
Introduction Large language models (LLMs) have demonstrated remarkable capabilities across a diverse range of natural language processing tasks, often achieving impressive performance through few-shot or zero-shot learning. This highlights the potential of LLMs to solve downstream tasks efficiently, leveraging their vast linguistic knowledge.
In this work, we explore the application of instruction-tuned LLMs, specifically Llama2, to enhance end-to-end automatic speech recognition (ASR) performance. We propose a novel integration of the LLM into a hybrid connectionist temporal classification (CTC) and attention-based ASR architecture, where the LLM serves as a front-end for the decoder network.
Methodology The key idea is to capitalize on the LLM's potential as a zero-shot grammatical error correction model. We first obtain an initial ASR hypothesis from the encoder output via CTC decoding. This hypothesis is then fed into the LLM, along with an explicit instruction to guide the model towards correcting grammatical errors. The decoder network subsequently takes the LLM embeddings as input, incorporating acoustic information from the encoder, to generate the final output sequence.
The proposed model is trained in two stages: 1) training a baseline hybrid CTC-attention-based end-to-end ASR model, and 2) training a new decoder network from scratch, using the pre-trained encoder and CTC networks, with the LLM as the front-end. The objective function is defined by the negative log-likelihood of the joint probability of the target sequence and the ASR hypothesis, where the intractable marginalization over hypotheses is approximated through sampling.
During inference, the most probable sequence is estimated by first obtaining the best ASR hypothesis using the encoder and CTC decoding, and then performing joint CTC and attention decoding with the LLM-enhanced decoder.
Experiments and Results We evaluated the proposed model on various English ASR tasks, including LibriSpeech (LS), TED-LIUM2 (TED2), and CoVoST2 (CV2), and compared its performance to the baseline hybrid CTC-attention model.
The results demonstrate that the proposed integration of the instruction-tuned LLM consistently outperformed the baseline model on tasks other than LS-960, with the most significant gains observed on the CV2 task. This can be attributed to the use of unnormalized written-style text in CV2, which enabled the LLM to extract more precise linguistic information.
In the LS-960 task, the proposed model showed limited improvements, which we attribute to the LLM's tendency to struggle with recognizing "uncommon" words, such as character names in audiobooks. This issue was mitigated to some extent by combining the proposed model with LLM-based rescoring, which further enhanced the performance across all tasks.
Ablation studies were conducted to assess the importance of the LLM integration and the prompt design. The results confirmed the effectiveness of the LLM in improving ASR performance and highlighted the crucial role of the prompt in maximizing the LLM's zero-shot learning capability.
Additionally, we compared the proposed approach with other methods for integrating LLMs into ASR, such as shallow fusion and rescoring. While these techniques also led to notable performance improvements, the proposed model's direct integration of the LLM into the decoder network demonstrated competitive or superior results.
Conclusion and Future Work In this work, we have presented a novel integration of an instruction-tuned LLM, Llama2, into an end-to-end ASR framework. By leveraging the LLM's zero-shot grammatical error correction capability, we were able to enhance the ASR performance across various tasks, particularly in domains where the linguistic information is crucial.
Future research directions may include exploring the application of the proposed model to other speech-related tasks, such as speech translation, and investigating methods to further address the LLM's limitations in handling uncommon words. Additionally, exploring alternative prompt designs and investigating the potential of few-shot or few-example fine-tuning of the LLM could lead to further performance improvements.
Overall, this work demonstrates the promising potential of harnessing the versatile linguistic knowledge embedded within instruction-tuned LLMs to advance the field of end-to-end speech recognition.