Abstract

This paper explores P¯an. inian Grammar (PG) as an information processing device in terms of ‘how’, ‘how much’ and ‘where’ languages encode information. PG is based on a morphologically rich language, Sanskrit. We apply PG on English and see how the P¯an. inian perspective would deal with it from the information theoretical point of view and its effectiveness in machine translation. We analyze English phrases defining sup (nominal inflections) and ti ˙n (finite verb inflections) and compare them with the notion of pada (an inflected word form) and samasta-pada (compound) in Sanskrit. Sanskrit encodes relations between nouns and adjectives and nouns in apposition through agreement between gender, number and case markers, whereas English encodes them through positions. As a result, constituents are formed. It appears that an English phrase contains more than one pada, hence, cannot be similar to a pada. However, we show the linguistic similarities between a pada, samasta-pada and ‘phrase’.

Abstract

We present here a data mining approach for part-of-speech (POS) tagging, an important natural language processing (NLP) task, which is a classification problem. We propose a semi-supervised associative classification method for POS tagging. Existing methods for building POS taggers require extensive domain and linguistic knowledge and resources. Our method uses a combination of a small POS tagged corpus and untagged text data as training data to build the classifier model using association rules. Our tagger works well with very little training data also. The use of semi-supervised learning provides the advantage of not requiring a large high-quality annotated corpus. These properties make it especially suitable for resource-poor languages. Our experiments on various resource-rich, resource-moderate and resource-poor languages show good performance without using any language-specific linguistic information. We note that inclusion of such features in our method may further improve the performance. Results also show that for smaller training data sizes our tagger performs better than state-of-the-art conditional random field (CRF) tagger using same features as our tagger.

Abstract

In this paper, we present hybrid approaches for pronominal reference type (abstract or concrete) identification and event anaphora resolution for Hindi. Pronominal reference type identification is one of the important parts for any anaphora resolution system as it helps anaphora resolver in optimal feature selection based on pronominal reference types. We use language specific rules and features in set of classifiers (ensemble learning) for pronominal type identification. We discuss event referring anaphors (pronouns) and their resolution using Paninian dependency grammar, language syntax, proximity of events, etc. We achieved around 9̃0% accuracy in the pronominal reference type identification and around 7̃1% F-score in the event anaphora resolution on Hindi dependency tree-bank corpus.

Abstract

Back off techniques are employed in syllable based unit selection speech synthesis systems to maintain the naturalness of the speech in spite of the missing syllables. In synthesizing the missing complex consonant clusters syllables of Telugu, we introduced reduced vowel epenthesis as a rule-based backoff strategy[1]. In this paper, we refine the scope of the approach in selectively applying vowel epenthesis only in cases of sonority rise between adjacent consonants. When the sonority does not rise (stop-stop, liquid-stop clusters), we increase the duration of the consonant. Owing to specific patterns of vowel epenthesis observed in languages, we conduct a subjective evaluation to determine the identity of the epenthetic vowel in Hindi. From the inferences of the listening test, we devise a class based rule to perform epenthesis. Further, to evaluate the performance of the designed system, we perform both subjective as well as an objective evaluation based on confidence measures from an ASR system. We conduct a phone level automatic speech recognition task on the intelligibility of the words synthesized using epenthesis as a cluster-repair strategy. The results show that the proposed back off method helps in producing more natural-sounding speech compared to the conventional backoffs.

Abstract

The objective of this work is to develop a rule-based emotion conversion method for a better emotional perception. In this work, performance of emotion conversion using the linear modification model is improved by using vowel-based non-uniform prosody modification. In the present approach, attempts were made to integrate features like position and identity for addressing the non-uniformity in prosody generated due to the emotional state of the speaker. We mainly concentrate on the parameters such as strength, duration and pitch contour of vowels at different parts of the sentence. The influence of emotions on the above parameters is exploited to convert the speech from neutral emotion to the target emotion. Non-uniform prosody modification factors for emotion conversion are based on the position of vowels in the word, and the position of the word in the sentence. This study is carried out by using Indian Institute of Technology-Simulated Emotion speech corpus. Evaluation of the proposed algorithm is carried out by a subjective listening test. From the listening tests, it is observed that the performance of the proposed approach is better than the existing approaches

Abstract

In this paper, deep neural networks are investigated for language identification in Indian languages. Deep neural networks(DNN) have been recently proposed for this task. However many architectural choices and training aspects that have been made while building such systems have not been studied carefully. We perform several experiments on a dataset consisting of 12 Indian languages with a total training data of about 120hours in evaluating the effect of such choices.While DNN based approach is inherently a frame based one, we propose an attention mechanism based DNN architecture for utterance level classification there by efficiently making use of the context. Evaluation of models were performed on 30 hours of testing data with 2.5 hours for each language. In our results, we find that deeper architectures outperform shallower counterparts. Also, DNN with attention mechanism out-performs the regular DNN models indicating the effectiveness of attention mechanism.

Abstract

In this paper, we describe a statistical parametric speech synthesis approach with unit-level acoustic representation. In conventional deep neural network based speech synthesis, the input text features are repeated for the entire duration of phoneme for mapping text and speech parameters. This mapping is learnt at the frame-level which is the de-facto acoustic representation. However much of this computational requirement can be drastically reduced if every unit can be represented with a fixed-dimensional representation. Using recurrent neural network based auto-encoder, we show that it is indeed possible to map units of varying duration to a single vector. We then use this acoustic representation at unit-level to synthesize speech using deep neural network based statistical parametric speech synthesis technique. Results show that the proposed approach is able to synthesize at the same quality as the conventional frame based approach at a highly reduced computational cost.

Abstract

Shouted speech signals have been studied mostlyfor utterances or word segments. In the production features derived over these segments, the differences between normal and shouted speech may sometimes be masked by variations due to pauses and unvoiced regions. Also, our recent study of electroglottograph signals has highlighted the usefulness of examining changes in the glottal excitation source characteristics during production of shouted speech. In this paper we examine changes in the shout production features in the vowel regions,that are found automatically by using the knowledge of accurate vowel onset points and epochs. Vowel regions are produced by periodic impulse-like excitation and contain relatively high signal energy. Hence the source features the instantaneous fundamental frequency and strength of excitation are examined, along with the filter feature the dominant frequency, for five different steady vowel regions. Changes in these features, that distinguish between normal and shouted speech, are more prominent in the automatically found vowel regions than for the entire word segments. This insight can help improving the automatic shout detection and other paralinguistic applications where acoustic cues need to be found in the vowel regions, e.g., detection of emotion categories.

Abstract

Automatic detection of shout prosody in continuous speech signal involves examining changes in its production characteristics. Our recent study of electroglottograph signals highlighted that significant changes occur in the glottal excitation source characteristics during production of shouted speech, especially in the vowel contexts. But the differences between normal and shouted speech, in the production features derived over utterances or word segments, may be masked sometimes by pauses or unvoiced regions related variations. Also, for such a real-time system, these vowel regions need to be found automatically. In this paper, changes in the shout production features are examined in the automatically detected vowel regions. Production of a vowel involves periodic impulse-like excitation and relatively high signal energy. Hence, the knowledge of epochs using zero-frequency filtering, and accurate vowel onset points can be used for detecting these regions. Changes in two excitation source features, the instantaneous fundamental frequency and strength of excitation, and in a vocal tract filter feature the dominant frequency, are examined for five steady vowel regions. Larger changes in these distinguishing features are observed in the automatically found vowel regions, than in word segments. This approach can help improving the systems for automatic detection of shout regions in continuous speech, and in paralinguistic applications that involve detection of prosody or emotions

Abstract

In this paper, we analyze the performance of various sequence to sequence neural networks on the task of grapheme to phoneme (G2P) conversion. G2P is a very important component in applications like text-to-speech, automatic speech recognition etc,. Because the number of graphemes that a word consists of and the corresponding number of phonemes are different, they are first aligned and then mapped. With the recent advent of sequence to sequence neural networks, the alignment step can be skipped allowing us to directly map the input and output sequences. Although the sequence to sequence neural nets have been applied for this task very recently, there are some questions concerning the architecture that need to be addressed. We show in this paper that, complex recurrent neural network units (like long-short term memory cells) may not be required to achieve good performance on this task. Instead simple recurrent neural networks (RNN) will suffice. We also show that the encoder can be a uni-directional RNN as opposed to the usually preferred bi-directional RNN. Further, our experiments reveal that encoder-decoder models with soft-alignment outperforms fixed vector context counterpart. The results demonstrate that with very few parameters we can indeed achieve comparable performance to much more complicated architectures.