A new way to enhance speech signal based on compressed sensing

FPGA Implementation of Dynamic Quantile Tracking based Noise Estimation for Speech Enhancement

In hands-free mobile communication, speech quality is often degraded due to presence of surrounding noise. This paper introduces an improved version of Minimum Mean Square Error (MMSE) noise estimator. Noise spectrum estimation is a crucial element used in speech recognition systems. Our proposed noise estimation method is based on a popular searching algorithm used in software engineering called Binary Search (BS), which we integrate with First-In First-Out (FIFO) MMSE noise reduction algorithm. In the literature, there is no research addressing the integration of BS algorithm with an active noise cancellation system. The noise spectral minima are computed using BS algorithm which makes it fast and efficient. The proposed algorithm is tested using real time data collected from vehicles running at different speeds. Simulation results are provided, and it is shown that the proposed algorithm outperforms other MMSE algorithms.

Speech enhancement using new iterative minimum statistics approach

Recently, two major types of Power-Normalized Cepstral Coefficients (PNCCs) were proposed as noise robust Automatic Speech Recognition (ASR) front-end. All the literatures for these two PNCCs assume clean training data and clean or noisy test data. However, we find that one PNCC method has good performance for the clean training/noisy test scenario, but degrades when test data is cleaner than the training data. The other PNCC method performs relatively better for noisy training/clean test conditions, but is not very robust for the clean training/noisy test conditions. We propose Combined PNCC (C-PNCC) algorithm, which is superior to both previous PNCCs for clean training/noisy test cases, and which also has reasonably good performance for noisy training/clean test conditions.

Combined PNCC feature extractor for robust speech recognition

: The main objective of this research was to explore and refine methods for detailed phonetic labeling (English or Russian) and character level labeling (Mandarin). Much of the work involved new front end signal processing methods designed to improve acoustic phonetic representations for speech. This resulted in recognition rates for TIMIT (English) of 74%, among the highest reported in the literature. A complete character recognition system for Mandarin was developed and tested. Character recognition rates as high as 88% were obtained, using an approximately 40 training databases. For the case of Russian, a system for automatically converting Russian to morphemes, a kind of base syllable, was created and tested. A suite of tools for front end processing, automatic forced alignment, and a complete automatic recognition system are described.

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA614725&Location=U2&doc=GetTRDoc.pdf

Detailed Phonetic Labeling of Multi-language Database for Spoken Language Processing Applications

This paper deals with configuration of an algorithm to be used in a speech-passing angle grinder noise-canceling headset. Angle grinder noise is annoying and interrupts ordinary oral communication. Meaning that, low SNR noisy condition is ahead. Since variation in angle grinder working condition changes noise statistics, the noise will be nonstationary with possible jumps in its power. Studies are conducted for picking an appropriate algorithm. A modified version of the well-known spectral subtraction shows superior performance against alternate methods. Noise estimation is calculated through a multi-band fast adapting scheme. The algorithm is adapted very quickly to the non-stationary noise environment while inflecting minimum musical noise and speech distortion on the processed signal. Objective and subjective measures illustrating the performance of the proposed method are introduced.

/pdf/improved-noise-minimum-statistics-estimation-algorithm-for-4fpvlajuid.pdf

Improved noise minimum statistics estimation algorithm for using in a speech-passing noise-rejecting headset

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Improved noise minimum statistics estimation algorithm for using in a speech-passing noise-rejecting headset