Multi-Head State Space Model for Speech Recognition (2305.12498v2)

Abstract: State space models (SSMs) have recently shown promising results on small-scale sequence and LLMling tasks, rivalling and outperforming many attention-based approaches. In this paper, we propose a multi-head state space (MH-SSM) architecture equipped with special gating mechanisms, where parallel heads are taught to learn local and global temporal dynamics on sequence data. As a drop-in replacement for multi-head attention in transformer encoders, this new model significantly outperforms the transformer transducer on the LibriSpeech speech recognition corpus. Furthermore, we augment the transformer block with MH-SSMs layers, referred to as the Stateformer, achieving state-of-the-art performance on the LibriSpeech task, with word error rates of 1.76\%/4.37\% on the development and 1.91\%/4.36\% on the test sets without using an external LLM.

Enhancing Speech Recognition with Multi-Head State Space Models

Introduction

The field of speech recognition has observed substantial innovations and advancements with the deployment of various deep learning architectures. Among the notable contributions, the Transformer model, with its self-attention mechanism, has significantly dominated the field, providing state-of-the-art performance across numerous tasks. However, this paper introduces a novel approach by incorporating Multi-Head State Space Models (MH-SSMs) into the acoustic encoder of a neural network transducer model. The authors present a structured investigation into the ramifications of integrating MH-SSMs, focusing on their performance in speech recognition tasks, particularly on the LibriSpeech corpus.

State Space Models: The Linear RNN Alternative

Central to this paper is the exploration of State Space Models (SSMs) as efficient alternatives to Recurrent Neural Networks (RNNs) and attention mechanisms. Traditionally, SSMs, with their capacity to model continuous or discrete systems, have not been extensively applied in sequence modeling due to their computational intensity. This research extends upon existing SSM frameworks by introducing a multi-head configuration - MH-SSM, enriched with a unique gating mechanism, positing that it could provide a balanced modeling of temporal dynamics in speech sequences.

Key Contributions

The paper delineates three primary technical contributions that underpin their proposed MH-SSM architecture:

1. Stacked and Multi-Head Generalization: The authors generalize the SSM approach by allowing linear projection of signals into multiple heads, which are processed by independent SSMs. This multi-head configuration enables the model to capture a richer set of temporal dynamics.
2. Head Gating: Introduction of an inter-head gating mechanism, where outputs from different SSM heads can gate each other, fostering inter-head communication and thereby enriching the model's expressivity.
3. Combining with Attention: The paper also explores augmenting the Transformer encoder with a bidirectional SSM block, named Stateformer, showcasing how SSMs can be seamlessly integrated with attention mechanisms to enhance performance.

Theoretical Implications and Practical Outcomes

On a theoretical level, this research underscores the potential of state space models in sequence modeling, asserting an alternative pathway to attention-based and recurrent models. By demonstrating that MH-SSMs can effectively capture both local and global temporal dependencies in speech signals, the paper paves the way for further exploration into attention-free models in various sequence modeling domains beyond speech recognition.

Practically, the proposed MH-SSM and Stateformer architectures were evaluated against strong baselines on the LibriSpeech corpus. Without the reliance on external LLMs, MH-SSM achieved competitive word error rates (WER) of 1.80%/4.96% on development and 2.01%/4.61% on test sets, significantly surpassing traditional Transformer models. More impressively, the Stateformer architecture further advances performance, achieving WERs of 1.76%/4.37% on development and 1.91%/4.36% on test sets, rivaling and in some cases outperforming state-of-the-art models. These results not only validate the efficacy of the MH-SSM and Stateformer but also highlight their potential as high-performing, attention-free alternatives for speech recognition tasks.

Future Directions in AI Research

The successful incorporation of MH-SSMs into speech recognition models represents a novel exploration of leveraging state space theory in deep learning. This approach opens up new avenues for research, especially in tasks where modeling long-range dependencies is crucial. Future work may explore the applicability of MH-SSMs across a wider range of sequence modeling tasks, such as language translation, time-series forecasting, and more. Moreover, further refinement of the gating mechanisms and integration strategies with existing architectures could yield even more powerful and efficient models, potentially reducing the computational overhead associated with attention mechanisms.

In conclusion, this paper introduces a promising new architecture that leverages the strengths of state space models, presenting a compelling alternative to conventional models in the field of speech recognition. The remarkable performance of MH-SSM and Stateformer architectures as attention-free models hints at a broader applicability in sequence modeling, setting the stage for future explorations in AI research.