Speech Recognition with Deep Recurrent Neural Networks

Synopsis

Overview

• Keywords: Speech Recognition, Deep Learning, Recurrent Neural Networks, Long Short-Term Memory, Connectionist Temporal Classification
• Objective: Investigate the effectiveness of deep recurrent neural networks, specifically Long Short-Term Memory (LSTM) networks, for speech recognition tasks.
• Hypothesis: Deep LSTM networks can outperform traditional deep feedforward networks in phoneme recognition tasks due to their ability to leverage long-range context.

Background

• Preliminary Theories:

  • Recurrent Neural Networks (RNNs): A class of neural networks designed for sequential data, capable of maintaining a hidden state that captures information from previous inputs.
  • Long Short-Term Memory (LSTM): An advanced RNN architecture that includes memory cells to manage long-range dependencies and mitigate the vanishing gradient problem.
  • Connectionist Temporal Classification (CTC): A training method for RNNs that allows them to learn from unsegmented input sequences, crucial for tasks like speech recognition where input-output alignment is not predefined.
  • Bidirectional RNNs (BRNNs): RNNs that process data in both forward and backward directions, enhancing context utilization for better predictions.

• Prior Research:

  • 1994: Introduction of hybrid models combining neural networks with hidden Markov models for speech recognition.
  • 2012: Deep feedforward networks demonstrated significant improvements in acoustic modeling, setting a new standard in speech recognition.
  • 2012: Development of CTC, enabling end-to-end training of RNNs for sequence labeling without requiring explicit alignment.
  • 2012: RNN transducers introduced, combining CTC with a language model to improve phoneme prediction.

Methodology

• Key Ideas:

  • Deep LSTM Architecture: Stacking multiple LSTM layers to create a deep network that can learn complex representations of sequential data.
  • End-to-End Training: Directly mapping acoustic inputs to phonetic outputs without predefined alignments, enhancing flexibility and performance.
  • Weight Noise Regularization: Adding Gaussian noise to weights during training to prevent overfitting and improve generalization.

• Experiments:

  • Evaluated multiple RNN configurations on the TIMIT phoneme recognition dataset, varying the number of hidden layers and the training methods (CTC, RNN transducer).
  • Used a Fourier-transform-based filter-bank for audio data preprocessing, resulting in input vectors of size 123.
  • Measured performance using phoneme error rate (PER) as the primary metric.

• Implications: The methodology allows for more robust training of RNNs in speech recognition, potentially leading to improved accuracy and generalization in real-world applications.

Findings

• Outcomes:

  • Deep LSTM networks achieved a state-of-the-art phoneme error rate of 17.7% on the TIMIT dataset, outperforming previous models.
  • The depth of the network was found to be more critical than the size of individual layers, confirming previous findings in deep learning.
  • Bidirectional LSTMs showed slight advantages over unidirectional models, particularly in capturing context from both past and future inputs.

• Significance: This research highlights the potential of deep LSTMs in speech recognition, challenging the dominance of deep feedforward networks and demonstrating the effectiveness of end-to-end training approaches.

• Future Work: Suggestions include extending the system to large vocabulary speech recognition and integrating convolutional neural networks with LSTM architectures for enhanced performance.

• Potential Impact: Advancements in these areas could lead to significant improvements in speech recognition systems, making them more accurate and applicable across various languages and dialects.