Automatic syllabification using segmental conditional random fields

In this paper we present a statistical approach for the automatic syllabification of phonetic word transcriptions. A syllable bigram language model forms the core of the system. Given the large number of syllables in non-syllabic languages, sparsity is the main issue, especially since the available syllabified corpora tend to be small. Traditional back-off mechanisms only give a partial solution to the sparsity problem. In this work we use a set of features for back-off purposes: on the one hand probabilities such as consonant cluster probabilities, and on the other hand a set of rules based on generic syllabification principles such as legality, sonority and maximal onset. For the combination of these highly correlated features with the baseline bigram feature we employ segmental conditional random fields (SCRFs) as statistical framework. The resulting method is very versatile and can be used for any amount of data of any language. The method was tested on various datasets in English and Dutch with dictionary sizes varying between 1 and 60 thousand words. We obtained a 97.96% word accuracy for supervised syllabification and a 91.22% word accuracy for unsupervised syllabification for English. When including the top-2 generated syllabifications for a small fraction of the words, virtual perfect syllabification is obtained in supervised mode

Phone recognition using Restricted Boltzmann Machines

For decades, Hidden Markov Models (HMMs) have been the state-of-the-art technique for acoustic modeling despite their unrealistic independence assumptions and the very limited representational capacity of their hidden states. Conditional Restricted Boltzmann Machines (CRBMs) have recently proved to be very effective for modeling motion capture sequences and this paper investigates the application of this more powerful type of generative model to acoustic modeling. On the standard TIMIT corpus, one type of CRBM outperforms HMMs and is comparable with the best other methods, achieving a phone error rate (PER) of 26.7 % on the TIMIT core test set. Index Terms — phone recognition, restricted Boltzmann machines, distributed representations

Broad phonetic class definition driven by phone confusions

Intermediate representations between the speech signal and phones may be used to improve discrimination among phones that are often confused. These representations are usually found according to broad phonetic classes, which are defined by a phonetician. This article proposes an alternative data-driven method to generate these classes. Phone confusion information from the analysis of the output of a phone recognition system is used to find clusters at high risk of mutual confusion. A metric is defined to compute the distance between phones. The results, using TIMIT data, show that the proposed confusion-driven phone clustering method is an attractive alternative to the approaches based on human knowledge. A hierarchical classification structure to improve phone recognition is also proposed using a discriminative weight training method. Experiments show improvements in phone recognition on the TIMIT database compared to a baseline system

Efficient Algorithms for Fast Integration on Large Data Sets from Multiple Sources

Background Recent large scale deployments of health information technology have created opportunities for the integration of patient medical records with disparate public health, human service, and educational databases to provide comprehensive information related to health and development. Data integration techniques, which identify records belonging to the same individual that reside in multiple data sets, are essential to these efforts. Several algorithms have been proposed in the literatures that are adept in integrating records from two different datasets. Our algorithms are aimed at integrating multiple (in particular more than two) datasets efficiently. Methods Hierarchical clustering based solutions are used to integrate multiple (in particular more than two) datasets. Edit distance is used as the basic distance calculation, while distance calculation of common input errors is also studied. Several techniques have been applied to improve the algorithms in terms of both time and space: 1) Partial Construction of the Dendrogram (PCD) that ignores the level above the threshold; 2) Ignoring the Dendrogram Structure (IDS); 3) Faster Computation of the Edit Distance (FCED) that predicts the distance with the threshold by upper bounds on edit distance; and 4) A pre-processing blocking phase that limits dynamic computation within each block. Results We have experimentally validated our algorithms on large simulated as well as real data. Accuracy and completeness are defined stringently to show the performance of our algorithms. In addition, we employ a four-category analysis. Comparison with FEBRL shows the robustness of our approach. Conclusions In the experiments we conducted, the accuracy we observed exceeded 90% for the simulated data in most cases. 97.7% and 98.1% accuracy were achieved for the constant and proportional threshold, respectively, in a real dataset of 1,083,878 records

Boosting End-to-End Multilingual Phoneme Recognition through Exploiting Universal Speech Attributes Constraints

We propose a first step toward multilingual end-to-end automatic speech recognition (ASR) by integrating knowledge about speech articulators. The key idea is to leverage a rich set of fundamental units that can be defined "universally" across all spoken languages, referred to as speech attributes, namely manner and place of articulation. Specifically, several deterministic attribute-to-phoneme mapping matrices are constructed based on the predefined set of universal attribute inventory, which projects the knowledge-rich articulatory attribute logits, into output phoneme logits. The mapping puts knowledge-based constraints to limit inconsistency with acoustic-phonetic evidence in the integrated prediction. Combined with phoneme recognition, our phone recognizer is able to infer from both attribute and phoneme information. The proposed joint multilingual model is evaluated through phoneme recognition. In multilingual experiments over 6 languages on benchmark datasets LibriSpeech and CommonVoice, we find that our proposed solution outperforms conventional multilingual approaches with a relative improvement of 6.85% on average, and it also demonstrates a much better performance compared to monolingual model. Further analysis conclusively demonstrates that the proposed solution eliminates phoneme predictions that are inconsistent with attributes