A Comparative Study Of LPCC And MFCC Features For The Recognition Of Assamese Phonemes
نویسنده
چکیده
In this paper two popular feature extraction techniques Linear Predictive Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) have been investigated and their performances have been evaluated for the recognition of Assamese phonemes. A multilayer perceptron based baseline phoneme recognizer has been built and all the experiments have been carried out using that recognizer. In the present study, attempt has been made to evaluate the performance of the speech recognition system with different feature set in quiet environmental condition as well as at different level of noise. It has been observed that at noise free operating environment when same speaker is used for training and testing the system, the system given 100% recognition accuracy for the recognition of Assamese phones for both the feature set. However, the performance of the system degrades considerably with increase in environmental noise level.It has been observed that the performance of LPCC based system degrades more rapidly compare to MFCC based system under environmental noise condition whereas under speaker variability conditions, LPCC shows relative robustness compare to MFCC though the performance of both the systems degrades considerably. Key Terms: Speech Recognition, LPCC, MFCC, MLP
منابع مشابه
LPC and MFCC Analysis of Assamese Vowel Phonemes
A speech signal contains many levels of information. Speech conveys the information about the language being spoken, the emotion, gender, and the identity of the speaker. Features parameters extracted from speech are very useful for speaker recognition as well as speech recognition. In this paper, the features LPC and MFCC are computed of Assamese vowel phonemes which will be helpful to develop...
متن کاملA Study on the Effect of Pitch on LPCC and PLPC Features for Children's ASR in Comparison to MFCC
In this work, following our previous studies, we study and quantify the effect of pitch on LPCC and PLPC features and explore their efficacy for children’s mismatched ASR in comparison to MFCC. Our analysis shows that, unlike MFCC, LPCC feature has no major influence of pitch variations. On the other hand, similar to MFCC, though PLPC is also found to be significantly effected by pitch variatio...
متن کاملSpeech Emotion Recognition Based on Power Normalized Cepstral Coefficients in Noisy Conditions
Automatic recognition of speech emotional states in noisy conditions has become an important research topic in the emotional speech recognition area, in recent years. This paper considers the recognition of emotional states via speech in real environments. For this task, we employ the power normalized cepstral coefficients (PNCC) in a speech emotion recognition system. We investigate its perfor...
متن کاملSpeech recognition of mandarin syllables using both linear predict coding cepstra and Mel frequency cepstra
This paper is to compare two most common features representing a speech word for speech recognition on the basis of accuracy, computation time, complexity and cost. The two features to represent a speech word are the linear predict coding cepstra (LPCC) and the Mel-frequency cepstrum coefficient (MFCC). The MFCC was shown to be more accurate than the LPCC in speech recognition using the dynamic...
متن کاملAutomatic Speaker Recognition using LPCC and MFCC
A person's voice contains various parameters that convey information such as emotion, gender, attitude, health and identity. This report talks about speaker recognition which deals with the subject of identifying a person based on their unique voiceprint present in their speech data. Pre-processing of the speech signal is performed before voice feature extraction. This process ensures the voice...
متن کامل