Indonesian Automatic Speech Recognition For Command Speech Controller Multimedia Player

The purpose of multimedia devices development is controlling through voice. Nowdays voice that can be recognized only in English. To overcome the issue, then recognition using Indonesian language model and accousticc model and dictionary. Automatic Speech Recognizier is build using engine CMU Sphinx with modified english language to Indonesian Language database and XBMC used as the multimedia player. The experiment is using 10 volunteers testing items based on 7 commands. The volunteers is classifiedd by the genders, 5 Male & 5 female. 10 samples is taken in each command, continue with each volunteer perform 10 testing command. Each volunteer also have to try all 7 command that already provided. Based on percentage clarification table, the word “Kanan†had the most recognize with percentage 83% while “pilih†is the lowest one. The word which had the most wrong clarification is “kembali†with percentagee 67%, while the word “kanan†is the lowest one. From the result of Recognition Rate by male there are several command such as “Kembaliâ€, “Utamaâ€, “Atas “ and “Bawah†has the low Recognition Rate. Especially for “kembali†cannot be recognized as the command in the female voices but in male voice that command has 4% of RR this is because the command doesn’t have similar word in english near to “kembali†so the system unrecognize the command. Also for the command “Pilih†using the female voice has 80% of RR but for the male voice has only 4% of RR. This problem is mostly because of the different voice characteristic between adult male and female which male has lower voice frequencies (from 85 to 180 Hz) than woman (165 to 255 Hz).The result of the experiment showed that each man had different number of recognition rate caused by the difference tone, pronunciation, and speed of speech. For further work needs to be done in order to improving the accouracy of the Indonesian Automatic Speech Recognition system.Keywords: Automatic Speech Recognizer, Indonesian Acoustic Model, CMU Sphinx, indonesian Language Model, Recognition Rate, XBMC

Phonetic Temporal Neural Model for Language Identification

Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions.Comment: Submitted to TASL

Cloud-based Automatic Speech Recognition Systems for Southeast Asian Languages

This paper provides an overall introduction of our Automatic Speech Recognition (ASR) systems for Southeast Asian languages. As not much existing work has been carried out on such regional languages, a few difficulties should be addressed before building the systems: limitation on speech and text resources, lack of linguistic knowledge, etc. This work takes Bahasa Indonesia and Thai as examples to illustrate the strategies of collecting various resources required for building ASR systems.Comment: Published by the 2017 IEEE International Conference on Orange Technologies (ICOT 2017

Zero resource speech synthesis using transcripts derived from perceptual acoustic units

Zerospeech synthesis is the task of building vocabulary independent speech synthesis systems, where transcriptions are not available for training data. It is, therefore, necessary to convert training data into a sequence of fundamental acoustic units that can be used for synthesis during the test. This paper attempts to discover, and model perceptual acoustic units consisting of steady-state, and transient regions in speech. The transients roughly correspond to CV, VC units, while the steady-state corresponds to sonorants and fricatives. The speech signal is first preprocessed by segmenting the same into CVC-like units using a short-term energy-like contour. These CVC segments are clustered using a connected components-based graph clustering technique. The clustered CVC segments are initialized such that the onset (CV) and decays (VC) correspond to transients, and the rhyme corresponds to steady-states. Following this initialization, the units are allowed to re-organise on the continuous speech into a final set of AUs in an HMM-GMM framework. AU sequences thus obtained are used to train synthesis models. The performance of the proposed approach is evaluated on the Zerospeech 2019 challenge database. Subjective and objective scores show that reasonably good quality synthesis with low bit rate encoding can be achieved using the proposed AUs

Shared-hidden-layer Deep Neural Network for Under-resourced Language the Content

Training speech recognizer with under-resourced language data still proves difficult. Indonesian language is considered under-resourced because the lack of a standard speech corpus, text corpus, and dictionary. In this research, the efficacy of augmenting limited Indonesian speech training data with highly-resourced-language training data, such as English, to train Indonesian speech recognizer was analyzed. The training was performed in form of shared-hidden-layer deep-neural-network (SHL-DNN) training. An SHL-DNN has language-independent hidden layers and can be pre-trained and trained using multilingual training data without any difference with a monolingual deep neural network. The SHL-DNN using Indonesian and English speech training data proved effective for decreasing word error rate (WER) in decoding Indonesian dictated-speech by achieving 3.82% absolute decrease compared to a monolingual Indonesian hidden Markov model using Gaussian mixture model emission (GMM-HMM). The case was confirmed when the SHL-DNN was also employed to decode Indonesian spontaneous-speech by achieving 4.19% absolute WER decrease