Shyamal Kr. Das Mandal

Critical issues in near net shape forming via green machining of ceramics: A case study of alumina dental crown

Abstract Green machining of ceramics through Computer Numerical Control (CNC) is efficient for near net shape fabrication due to minimum consumption of energy, less tool wear, and high material removal rate. However, there are numerous critical issues need to be addressed while manufacturing customized components through green state machining including fabrication of machinable green ceramics, designing of suitable mold for slurry casting, manufacture of suitable sample holders for mounting the fragile green samples during machining, and designing of machining tools for good surface finishing. This article introspects these critical issues and its possible solutions for efficient fabrication of dental crowns as a case study via green state machining. Highly loaded (55 vol%) alumina slurry was prepared for the fabrication of machinable dense alumina compacts by Protein Coagulation Casting (PCC) technique. Cylindrical alumina compacts were fabricated by casting alumina slurry into the polyvinyl chloride (PVC) mold. Metallic cylindrical sample holder was fabricated for mounting the green alumina samples for CNC machining. Diamond impregnated tool (∼3 mm diameter) was used for near net shaping of dental crown by grinding/milling. Dental crown (incisor) was successfully fabricated by optimizing different machining parameters.

Word boundary detection based on suprasegmental features: A case study on Bangla speech

This paper proposes a method for detecting word boundaries in continuous speech signal for Standard Colloquial Bengali (SCB), commonly referred to as Bangla. Bangla is a bound stress language with stress on the first syllable. Stress introduces its signature on the supra-segmental parameters of the speech signal, which may help to detect the word boundary in the continuous speech signal. The parameters used in this present study are: (1) Difference of the nucleus vowel duration across the syllable boundary, (2) Difference of the normalized nucleus vowel power across the syllable boundary, (3) Normalized F0 difference across the syllable boundary, (4) Difference of the average normalized F0 across the syllable boundary, (5) Difference of the normalized maximum periodic power of nucleus vowels across the syllable boundary, (6) Onset duration of the nucleus vowel. Altogether 225 sentences spoken by five native Bangla informants of both the sexes, in the age group of 20–50 years in normal laboratory environment are used in this study. These sentences contain 2734 syllables and 1103 words, sentence terminal words being excluded. A recognition score of 87.8% with a classifier, based on a distance function, weighted by inverse of variance is reported. Both speaker dependent as well as speaker independent studies are included.

Occurrence and duration modeling of sentence medial pause for Bangla text reading at different speech rate

One of the key issues for speech prosody is control of pause occurrence and duration. Occurrence of pause is unconditional at sentence boundaries with high probability at major syntactic boundaries such as clause boundaries and more or less arbitrarily at minor syntactic boundaries for text-readout speech. In this paper a detailed investigation is conducted for sentence-medial pauses for readout speech of Bangla for different speech rates. Based on the analysis, linear models with variables of syntactic unit length in terms of number of syllable and distance to directly modifying word are constructed for pause occurrence and duration for different speech rates. The models are evaluated using the test data which are not included in the analyzed data in different speech rates. The results show that the proposed models can predict occurrence probability correctly on average 82% cases for all speech rate, and pause duration within ±100 ms for 79% of the cases.

Discourse prosody planning in native (L1) and nonnative (L2) (L1-Bengali) English: a comparative study

This paper conducts a comparative study between L1 and L2 (L1 Bengali) English discourse level speech planning to investigate differences between L1 and L2 English speaker groups in the organization of discourse-level speech planning. For this purpose, English speech of 10 L1 English and 40 L1 Bengali speakers of the same discourse are analyzed in terms of using prosodic and acoustic cues by applying hierarchical discourse prosody framework. From this analysis, between-group differences in discourse level speech planning are found through the speech rate, locations of discourse boundary breaks as well as size and scope of speech planning and chunking units. Result of analysis shows that the speech rate of L1 English speakers is higher than that of L2 English speakers, L2 English speakers contain more break boundary than that of the L1 English speakers at every discourse level in the organization, which exhibit the fact that L2 English speakers use more intermediate chunking units and larger scale planning units than that of L1 English speakers. Between-group differences are also found through the analysis of phrase component at prosodic phrase level and accent component at the prosodic word level. These findings can be attributed to L2 English speakers’ improper phrasing, improper word level prominence and the ambiguous difference between content words and function words. The study concludes that the deficiencies in English strategy for L1 Bengali speakers’ discourse-level speech planning compared to L1 English speakers are due to the influence of L1 (Bengali) prosody at the L2 discourse level.

Importance of Utterance Partitioning in SVM Classifier with GMM Supervectors for Text-Independent Speaker Verification

This paper compares performances between GMM-UBM classifier and SVM classifier with GMM supervector as the linear kernel for text-independent speaker verification. The MFCC feature set has been used for this comparison. Experimental evaluation was conducted on the POLYCOST database. The importance of utterance partitioning for training speech has been discussed. Results reveal that, without utterance partitioning, the accuracy of SVM classifier with GMM supervectors for small test segment is poor. For proper utterance partitioning of the training speech, the SVM classifier with GMM supervectors performs significantly better compared to GMM-UBM baseline. The detailed derivation of GMM supervector has also been discussed.

Acoustic analysis of English lexical stress produced by native (L1) Bengali speakers compared to native (L1) English speakers

English lexical stress is acoustically related to combination of fundamental frequency (F0), duration, intensity and vowel quality. Current study compares the use of these correlates by 10 L1 English and 20 L1 Bengali speakers to find out which correlates are most difficult for Bengali speakers to acquire. Results showed that English and Bengali speakers used the acoustic correlates of vowel duration, intensity and F0 in similar manner, but Bengali speakers produced significantly less English like stress patterns. English speakers reduced vowel duration significantly more in the unstressed vowels compared to Bengali speakers and degree of intensity and F0 increase in stressed vowels by English speakers was higher than that by Bengali speakers. Moreover Bengali speakers produced English like vowel quality in certain unstressed syllables, but in other cases there were significant differences in vowel quality across groups. This study supports the idea of interference from L1 to L2 (nonnative) phonology.

L1 Bengali Phonological Interference on L2 English - Analysis of Bengali AESOP Corpus

Every language has its own phonemic system, which holds unique as well as common features. A language shares some phonemes with other languages, but no two languages have the same phonemic inventory. Contrastive analysis is the field of study in which different phonemic systems are laid side by side to find out similarities and dissimilarities between the phonemes of the languages concerned. The purpose of this study is to derive which phonemes are used by the L1 Bengali speakers to recognize American English phonemes which are new and similar to their L1 Bengali phonology. The results of this study showed typical phonological problems of American English pronunciation by L1 Bengali speakers which will help to develop Computer Assisted Spoken Language Learning (CASLL) tool for faster acquisition of American English language speaking of L1 Bengali speakers.

The intonation of Bangla sentences: Declarative, Wh-questions and yes/no by using Empirical Mode of Decomposition

This paper investigates the intonational properties of different types of sentence by using EMD analysis of Bangla language. The study is about yes/no questions, wh-questions and declarative sentences. The speech material used for the present study is 45 read utterances recorded in laboratory conditions. The result shows that the intonation pattern for declarative sentences is falling, wh-questions also follow the same intonation pattern as declarative, but for yes/no its rising. The other parameters of intonation pattern such as stressed word and intermediate phrase also studied. The results of EMD analysis is then compared with the Bangla grammar and Fujisaki model, which are satisfactory.

Feature Set Extracted Using Frequency-Time Analysis Approach for Speaker Verification

This paper compares the feature sets extracted using time-frequency analysis approach and frequency-time analysis approach for text-independent speaker verification. Mel-frequency cepstral coefficient (MFCC) feature set is extracted using time-frequency analysis approach. Temporal energy subband cepstral coefficient (TESBCC) feature set is extracted using frequency time analysis approach. The verification system is built around the likelihood ratio test, using effective GMM for likelihood functions, a universal background model (UBM) for alternative speaker representation, and using a Bayesian adaptation to derive speaker models from UBM. Results reveal that the feature set extracted using frequency-time analysis approach performs significantly better compared to the feature set extracted using time-frequency analysis approach. The equal error rates of MFCC and TESBCC feature sets are 7.19% and 3.38% respectively.