Sparse, Hierarchical and Semi-Supervised Base Learning for Monaural Enhancement of Conversational Speech

نویسندگان

  • Felix Weninger
  • Martin Wöllmer
  • Björn W. Schuller
چکیده

We address the learning of noise bases in a monaural speaker-independent speech enhancement framework based on non-negative matrix factorization. Bases are estimated from training data in batch processing by means of hierarchical and non-hierarchical sparse coding, or determined during the speech enhancement process based on the divergence of the observed noisy speech signal and the speech base. In extensive test runs on the Buckeye corpus of highly spontaneous speech and the CHiME corpus of nonstationary real-life noise, we observe that semi-supervised learning of noise bases leads to overall best results while a-priori learning of noise bases is useful to speed up computation.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A New Method for Speech Enhancement Based on Incoherent Model Learning in Wavelet Transform Domain

Quality of speech signal significantly reduces in the presence of environmental noise signals and leads to the imperfect performance of hearing aid devices, automatic speech recognition systems, and mobile phones. In this paper, the single channel speech enhancement of the corrupted signals by the additive noise signals is considered. A dictionary-based algorithm is proposed to train the speech...

متن کامل

Combining Bottleneck-BLSTM and Semi-Supervised Sparse NMF for Recognition of Conversational Speech in Highly Instationary Noise

We address the speaker independent automatic recognition of spontaneous speech in highly variable noise by applying semisupervised sparse non-negative matrix factorization (NMF) for speech enhancement coupled with our recently proposed frontend utilizing bottleneck (BN) features generated by a bidirectional Long Short-Term Memory (BLSTM) recurrent neural network. In our evaluation, we unite the...

متن کامل

Speech Enhancement using Adaptive Data-Based Dictionary Learning

In this paper, a speech enhancement method based on sparse representation of data frames has been presented. Speech enhancement is one of the most applicable areas in different signal processing fields. The objective of a speech enhancement system is improvement of either intelligibility or quality of the speech signals. This process is carried out using the speech signal processing techniques ...

متن کامل

Speech enhancement based on hidden Markov model using sparse code shrinkage

This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM ...

متن کامل

Sparse Autoencoder Based Semi-Supervised Learning for Phone Classification with Limited Annotations

We propose the application of a semi-supervised learning method to improve the performance of acoustic modelling for automatic speech recognition with limited linguistically annotated material. Our method combines sparse autoencoders with feed-forward networks, thus taking advantage of both unlabelled and labelled data simultaneously through mini-batch stochastic gradient descent. We tested the...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012