Exploring Neural Transducers for End-to-End Speech Recognition (1707.07413v1)

Abstract: In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition. We show that, without any LLM, Seq2Seq and RNN-Transducer models both outperform the best reported CTC models with a LLM, on the popular Hub5'00 benchmark. On our internal diverse dataset, these trends continue - RNNTransducer models rescored with a LLM after beam search outperform our best CTC models. These results simplify the speech recognition pipeline so that decoding can now be expressed purely as neural network operations. We also study how the choice of encoder architecture affects the performance of the three models - when all encoder layers are forward only, and when encoders downsample the input representation aggressively.

• The paper empirically evaluates Connectionist Temporal Classification (CTC), RNN-Transducers, and Sequence-to-Sequence models for end-to-end automatic speech recognition (ASR).
• RNN-Transducers and Seq2Seq models demonstrated superior performance over CTC by implicitly modeling language without external models, simplifying the ASR pipeline.
• The study suggests RNN-Transducers are promising for future ASR due to their simpler architecture and robustness, highlighting trade-offs with streaming requirements and encoder design.

Exploring Neural Transducers for End-to-End Speech Recognition

The paper presented in "Exploring Neural Transducers for End-to-End Speech Recognition" offers a comprehensive empirical evaluation of three prominent models adapted for automatic speech recognition (ASR): Connectionist Temporal Classification (CTC), RNN-Transducers, and Sequence-to-Sequence (Seq2Seq) with attention mechanisms. This paper meticulously contrasts these methodologies, focusing on their efficacy in handling core challenges inherent to ASR, including alignment and sequence transduction, without heavy reliance on external LLMs.

Comparative Performance and Model Architecture

The paper highlights a significant comparative analysis across the three architectures on the well-known Hub5'00 benchmark, demonstrating that both RNN-Transducers and Seq2Seq models outperformed CTC models with external LLMs, a assertion substantiated by their results on both their diverse internal dataset and the public benchmark. This outcome underscores the capability of RNN-Transducers and Seq2Seq models to implicitly model the language from the provided training corpus without explicit external LLMing, a feat which simplifies the traditional ASR pipelines by consolidating decoding steps into purely neural network operations.

Key Differences and Assumptions

The empirical paper delineates the structural differentiation in the models. Notably, CTC assumes conditional independence between predictions when given an audio segment, a hypothesis that proves limiting for ASR tasks where contextuality is paramount. Conversely, RNN-Transducers and Seq2Seq models circumvent this limitation by integrating context-dependence into their architecture, thereby learning a more nuanced and informed LLM from data. These models, however, do have inherent design assumptions, such as monotonic input-output alignment and the choice between hard versus soft alignments, which impact their deployment and performance under real-time streaming conditions.

Implications and Future Directions

The results not only highlight the potential simplification of ASR decoding processes through the adoption of RNN-Transducers and Seq2Seq models but also stress the practical advantages of RNN-Transducers due to their simpler architecture and fewer hyperparameters during implementation. This advancement is pivotal, considering the trade-offs associated with streaming requirements, encoder architecture choices, and model training scalability. The paper posits that while attention models struggle with noise and irregular input sequences due to their expansive attention frameworks, RNN-Transducers maintain robustness under varying conditions.

Going forward, the delineation of encoder layer architecture, particularly the balance between downsampling and model accuracy, opens new avenues for optimizing ASR tasks. Moreover, by experimenting with forward-only encoders, the paper suggests potential for real-time deployment, although further refinement is required to match the non-streaming model effectiveness. This research privileges RNN-Transducers as potential frontrunners for future ASR developments, although further exploration into extending Seq2Seq models for more robust and versatile end-to-end systems without degradation in performance is encouraged.

In conclusion, the paper provides an insightful exposition into the modeling variabilities and their practical implications, setting a foundation for future research endeavors aimed at refining the performance and efficiency of neural transducers in ASR. By detailing the intricacies of model performance under diverse conditions, it highlights key considerations for the ongoing evolution of end-to-end speech recognition technologies.