[Paper Review] Sequence Transduction with Recurrent Neural Networks

Alex Graves|arXiv (Cornell University)|Nov 14, 2012

Natural Language Processing Techniques20 references1,292 citations

TL;DR

This paper introduces an end-to-end, differentiable sequence transduction model based on two recurrent neural networks (RNNs) that jointly model input-output and output-output dependencies, enabling alignment-free sequence-to-sequence learning. The RNN transducer achieves state-of-the-art performance on phoneme recognition (1.0 bits per phoneme log-loss, 23.2% error rate) by integrating acoustic and linguistic information without requiring pre-defined alignments or external language models.

ABSTRACT

Many machine learning tasks can be expressed as the transformation---or \emph{transduction}---of input sequences into output sequences: speech recognition, machine translation, protein secondary structure prediction and text-to-speech to name but a few. One of the key challenges in sequence transduction is learning to represent both the input and output sequences in a way that is invariant to sequential distortions such as shrinking, stretching and translating. Recurrent neural networks (RNNs) are a powerful sequence learning architecture that has proven capable of learning such representations. However RNNs traditionally require a pre-defined alignment between the input and output sequences to perform transduction. This is a severe limitation since \emph{finding} the alignment is the most difficult aspect of many sequence transduction problems. Indeed, even determining the length of the output sequence is often challenging. This paper introduces an end-to-end, probabilistic sequence transduction system, based entirely on RNNs, that is in principle able to transform any input sequence into any finite, discrete output sequence. Experimental results for phoneme recognition are provided on the TIMIT speech corpus.

Motivation & Objective

• Address the limitation of RNNs requiring pre-defined alignments between input and output sequences in sequence transduction tasks.
• Develop a differentiable, end-to-end system that can model variable-length output sequences without prior knowledge of alignment or output length.
• Integrate both input sequence representations and autoregressive output modeling to improve robustness to sequential distortions.
• Enable joint learning of acoustic and linguistic information for tasks like speech recognition and machine translation.
• Provide a scalable, differentiable framework for sequence-to-sequence learning that generalizes beyond speech recognition.

Proposed method

• Define a sequence transduction system using two RNNs: a context network processing the input sequence and a prediction network processing previously generated outputs.
• Construct a joint probability distribution over all possible alignments and output sequences using a differentiable scoring function.
• Introduce an extended output space including a null symbol (φ) to represent 'no output' and allow flexible alignment modeling.
• Train the model using stochastic gradient descent with backpropagation through time, optimizing the negative log-likelihood of the target sequence.
• Use a differentiable alignment mechanism that allows gradients to flow through both input and output sequences, enabling end-to-end training.
• Apply the transducer to raw spectrograms for end-to-end speech recognition, directly mapping input features to character sequences.

Experimental results

Research questions

• RQ1 Can a differentiable, end-to-end RNN-based model learn to transduce sequences without requiring pre-defined alignments between input and output?
• RQ2 How well can a joint modeling approach of input and output dependencies improve sequence transduction performance compared to methods like CTC?
• RQ3 To what extent can the model integrate acoustic and linguistic information to improve robustness to sequential distortions?
• RQ4 Can the transducer generalize to tasks with variable-length outputs, such as text-to-speech or machine translation?
• RQ5 How sensitive is the model’s output to input and previous outputs, and can this be visualized to understand learned dependencies?

Key findings

• The RNN transducer achieves a log-loss of 1.0 bits per phoneme and a test error rate of 23.2% on the TIMIT phoneme recognition task, outperforming both CTC (1.3 bits, 25.5%) and a standalone prediction network (4.0 bits, 72.9%).
• The model successfully learns temporal alignment between input spectrograms and output phonemes, visualized as 'time warping' in the attention heatmaps.
• The prediction network alone performs poorly (72.9% error rate), indicating that joint modeling with the context network is essential for performance.
• Sensitivity analysis reveals that the transducer is sensitive to long-range dependencies in both input and output sequences, with bidirectional context networks showing broader input sensitivity.
• The model captures common subsequence patterns (e.g., 'TH', 'HER') through short vertical segments in the output lattice, indicating effective sequence modeling.
• The transducer enables end-to-end speech recognition directly from raw spectrograms to character sequences, bypassing intermediate phonetic representations.