HMM Based Language Identification from Speech Utterances of Popular Indic Languages Using Spectral and Prosodic Features
Bibliographic record
Abstract
Language identification system (LID) is a system which automatically recognises the languages of short-term duration of unknown utterance of human beings. It recognises the discriminate features and reveals the language of utterance that belongs to. In this paper, we consider concatenated feature vectors of Mel Frequency Cepstral Coefficients (MFCC) and Pitch for designing LID. We design a reference model one for each language using 14-dimensional feature vectors using Hidden Markov model (HMM) then evaluate against all reference models of listed languages. The likelihood value of test sample feature vectors given in the evaluation is considered to decide the language of unknown utterance of test speech sample. In this paper we consider seven Indian languages for the experimental set up and the performance of system is evaluated. The average performance of the system is 89.31% and 90.63% for three states and four states HMM for 3sec test speech utterances respectively and also it is also observed that the system gives significant results with 3sec test speech for four state HMM even though we follow simple procedure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".