Automatic Speech Recognition for Indonesian using Linear Predictive Coding (LPC) and Hidden Markov Model (HMM)

Speech recognition is influential signal processing in communication technology. Speech recognition has allowed software to recognize the spoken word. Automatic speech recognition could be a solution to recognize the spoken word. This application was developed using Linear Predictive Coding (LPC) for feature extraction of speech signal and Hidden Markov Model (HMM) for generating the model of each the spoken word. The data of speech used for training and testing was produced by 10 speaker (5 men and 5 women) whose each speakers spoke 10 words and each of words spoken for 10 times. This research is tested using 10-fold cross validation for each pair LPC order and HMM states. System performance is measured based on the average accuracy testing from men and women speakers. According to the test results that the amount of HMM states affect the accuracy of system and the best accuracy is 94, 20% using LPC order =13 and HMM state=16

Automatic Speech Recognition for Indonesian using Linear Predictive Coding (LPC) and Hidden Markov Model (HMM)

Speech recognition is influential signal processing in communication technology. Speech recognition has allowed software to recognize the spoken word. Automatic speech recognition could be a solution to recognize the spoken word. This application was developed using Linear Predictive Coding (LPC) for feature extraction of speech signal and Hidden Markov Model (HMM) for generating the model of each the spoken word. The data of speech used for training and testing was produced by 10 speaker (5 men and 5 women) whose each speakers spoke 10 words and each of words spoken for 10 times. This research is tested using 10-fold cross validation for each pair LPC order and HMM states. System performance is measured based on the average accuracy testing from men and women speakers. According to the test results that the amount of HMM states affect the accuracy of system and the best accuracy is 94, 20% using LPC order =13 and HMM state=16

Parallel Reference Speaker Weighting for Kinematic-Independent Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion, the estimation of articulatory kinematics from an acoustic waveform, is a challenging but important problem. Accurate estimation of articulatory movements has the potential for significant impact on our understanding of speech production, on our capacity to assess and treat pathologies in a clinical setting, and on speech technologies such as computer aided pronunciation assessment and audio-video synthesis. However, because of the complex and speaker-specific relationship between articulation and acoustics, existing approaches for inversion do not generalize well across speakers. As acquiring speaker-specific kinematic data for training is not feasible in many practical applications, this remains an important and open problem. This paper proposes a novel approach to acoustic-to-articulatory inversion, Parallel Reference Speaker Weighting (PRSW), which requires no kinematic data for the target speaker and a small amount of acoustic adaptation data. PRSW hypothesizes that acoustic and kinematic similarities are correlated and uses speaker-adapted articulatory models derived from acoustically derived weights. The system was assessed using a 20-speaker data set of synchronous acoustic and Electromagnetic Articulography (EMA) kinematic data. Results demonstrate that by restricting the reference group to a subset consisting of speakers with strong individual speaker-dependent inversion performance, the PRSW method is able to attain kinematic-independent acoustic-to-articulatory inversion performance nearly matching that of the speaker-dependent model, with an average correlation of 0.62 versus 0.63. This indicates that given a sufficiently complete and appropriately selected reference speaker set for adaptation, it is possible to create effective articulatory models without kinematic training data

Recognizing Voice Over IP: A Robust Front-End for Speech Recognition on the World Wide Web

The Internet Protocol (IP) environment poses two relevant sources of distortion to the speech recognition problem: lossy speech coding and packet loss. In this paper, we propose a new front-end for speech recognition over IP networks. Specifically, we suggest extracting the recognition feature vectors directly from the encoded speech (i.e., the bit stream) instead of decoding it and subsequently extracting the feature vectors. This approach offers two significant benefits. First, the recognition system is only affected by the quantization distortion of the spectral envelope. Thus, we are avoiding the influence of other sources of distortion due to the encoding-decoding process. Second, when packet loss occurs, our front-end becomes more effective since it is not constrained to the error handling mechanism of the codec. We have considered the ITU G.723.1 standard codec, which is one of the most preponderant coding algorithms in voice over IP (VoIP) and compared the proposed front-end with the conventional approach in two automatic speech recognition (ASR) tasks, namely, speaker-independent isolated digit recognition and speaker-independent continuous speech recognition. In general, our approach outperforms the conventional procedure, for a variety of simulated packet loss rates. Furthermore, the improvement is higher as network conditions worsen.Publicad

Speech vocoding for laboratory phonology

Using phonological speech vocoding, we propose a platform for exploring relations between phonology and speech processing, and in broader terms, for exploring relations between the abstract and physical structures of a speech signal. Our goal is to make a step towards bridging phonology and speech processing and to contribute to the program of Laboratory Phonology. We show three application examples for laboratory phonology: compositional phonological speech modelling, a comparison of phonological systems and an experimental phonological parametric text-to-speech (TTS) system. The featural representations of the following three phonological systems are considered in this work: (i) Government Phonology (GP), (ii) the Sound Pattern of English (SPE), and (iii) the extended SPE (eSPE). Comparing GP- and eSPE-based vocoded speech, we conclude that the latter achieves slightly better results than the former. However, GP - the most compact phonological speech representation - performs comparably to the systems with a higher number of phonological features. The parametric TTS based on phonological speech representation, and trained from an unlabelled audiobook in an unsupervised manner, achieves intelligibility of 85% of the state-of-the-art parametric speech synthesis. We envision that the presented approach paves the way for researchers in both fields to form meaningful hypotheses that are explicitly testable using the concepts developed and exemplified in this paper. On the one hand, laboratory phonologists might test the applied concepts of their theoretical models, and on the other hand, the speech processing community may utilize the concepts developed for the theoretical phonological models for improvements of the current state-of-the-art applications