Supervised Initialization of LSTM Networks for Fundamental Frequency Detection in Noisy Speech Signals

TL;DR

This paper introduces a supervised initialization method for LSTM networks using an auto-associative network to improve fundamental frequency detection in noisy speech signals, enhancing accuracy and training efficiency.

Contribution

It presents a novel supervised initialization approach for LSTM networks, improving fundamental frequency detection in noisy speech over traditional random initialization.

Findings

Supervised initialization improves detection accuracy.

Enhanced training efficiency under noisy conditions.

Better performance across different noise levels.

Abstract

Fundamental frequency is one of the most important parameters of human speech, of importance for the classification of accent, gender, speaking styles, speaker identification, age, among others. The proper detection of this parameter remains as an important challenge for severely degraded signals. In previous references for detecting fundamental frequency in noisy speech using deep learning, the networks, such as Long Short-term Memory (LSTM) has been initialized with random weights, and then trained following a back-propagation through time algorithm. In this work, a proposal for a more efficient initialization, based on a supervised training using an Auto-associative network, is presented. This initialization is a better starting point for the detection of fundamental frequency in noisy speech. The advantages of this initialization are noticeable using objective measures for the accuracy of the detection and for the training of the networks, under the presence of additive white noise at different signal-to-noise levels.