Using multiple acoustic feature sets for speech recognition
نویسندگان
چکیده
منابع مشابه
Using multiple acoustic feature sets for speech recognition
In this paper, the use of multiple acoustic feature sets for speech recognition is investigated. The combination of both auditory as well as articulatory motivated features is considered. In addition to a voicing feature, we introduce a recently developed articulatory motivated feature, the spectrum derivative feature. Features are combined both directly using linear discriminant analysis (LDA)...
متن کاملA novel feature extraction using multiple acoustic feature planes for HMM-based speech recognition
This paper describes an attempt to extract multiple peripheral features of a point x(ti,fj) on a timespectrum (TS) pattern by observing n×n neighborhoods of the point, and to incorporate these peripheral features (MPFPs: multiple peripheral feature planes) into the feature extractor of a speech recognition system together with MFCC parameters. Two types of peripheral feature extractor, MPFP-KL ...
متن کاملAcoustic feature combination for speech recognition
In this thesis, the use of multiple acoustic features of the speech signal is considered for speech recognition. The goals of this thesis are twofold: on the one hand, new acoustic features are developed, on the other hand, feature combination methods are investigated in order to find an effective integration of the newly developed features into state-of-the-art speech recognition systems. The ...
متن کاملA novel feature-extraction for speech recognition based on multiple acoustic-feature planes
This paper describes an attempt to incorporate the tinctions of the auditory nerve system into the feature extractor of speech recognition. The tinctions include four types of well-known responses to sound stimuli: local peaks of spectrum in steady sound. ascending FM sound, descending FM sound, and sharply rising and falling sound. Each function is realized in the form of a three-level derivat...
متن کاملSpeech Recognition using Acoustic Landmarks and Binary Phonetic Feature Classifiers
In spite of decades of research, Automatic Speech Recognition (ASR) is far from reaching the goal of performance close to Human Speech Recognition (HSR). One of the reasons for unsatisfactory performance of the state-of-the-art ASR systems, that are based largely on Hidden Markov Models (HMMs), is the inferior acoustic modeling of low level or phonetic level linguistic information in the speech...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Speech Communication
سال: 2007
ISSN: 0167-6393
DOI: 10.1016/j.specom.2007.04.005