7 research outputs found
Don't Be So Sure! Boosting ASR Decoding via Confidence Relaxation
Automatic Speech Recognition (ASR) systems frequently use a search-based
decoding strategy aiming to find the best attainable transcript by considering
multiple candidates. One prominent speech recognition decoding heuristic is
beam search, which seeks the transcript with the greatest likelihood computed
using the predicted distribution. While showing substantial performance gains
in various tasks, beam search loses some of its effectiveness when the
predicted probabilities are highly confident, i.e., the predicted distribution
is massed for a single or very few classes. We show that recently proposed
Self-Supervised Learning (SSL)-based ASR models tend to yield exceptionally
confident predictions that may hamper beam search from truly considering a
diverse set of candidates. We perform a layer analysis to reveal and visualize
how predictions evolve, and propose a decoding procedure that improves the
performance of fine-tuned ASR models. Our proposed approach does not require
further training beyond the original fine-tuning, nor additional model
parameters. In fact, we find that our proposed method requires significantly
less inference computation than current approaches. We propose aggregating the
top M layers, potentially leveraging useful information encoded in intermediate
layers, and relaxing model confidence. We demonstrate the effectiveness of our
approach by conducting an empirical study on varying amounts of labeled
resources and different model sizes, showing consistent improvements in
particular when applied to low-resource scenarios.Comment: Accepted to AAAI 202
Magnitude or Phase? A Two Stage Algorithm for Dereverberation
In this work we present a new single-microphone speech dereverberation
algorithm. First, a performance analysis is presented to interpret that
algorithms focused on improving solely magnitude or phase are not good enough.
Furthermore, we demonstrate that few objective measurements have high
correlation with the clean magnitude while others with the clean phase.
Consequently ,we propose a new architecture which consists of two sub-models,
each of which is responsible for a different task. The first model estimates
the clean magnitude given the noisy input. The enhanced magnitude together with
the noisy-input phase are then used as inputs to the second model to estimate
the real and imaginary portions of the dereverberated signal. A training scheme
including pre-training and fine-tuning is presented in the paper. We evaluate
our proposed approach using data from the REVERB challenge and compare our
results to other methods. We demonstrate consistent improvements in all
measures, which can be attributed to the improved estimates of both the
magnitude and the phase