Towards Burmese (Myanmar) Morphological Analysis

Ding, Chenchen; Aye, Hnin Thu Zar; Pa, Win Pa; Nwet, Khin Thandar; Soe, Khin Mar; Utiyama, Masao; Sumita, Eiichiro

doi:10.1145/3325885

Cited by 20 publications

(4 citation statements)

References 26 publications

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“…After the voice has split, they do not convert it to text. For creating strong acoustic models for speech recognition, accurate phonetic transcriptions are required [68,69]. After increasing the expected voice, IVSE converts the voice into text to guarantee that the converted text matches the original voice's text.…”

Section: Reason For Choosing Lightgbmmentioning

confidence: 99%

IVSE: Indian Voice Separation and Enhancement from a cocktail party scenario

Gupta

Singh²,

Singh

2023

Preprint

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Audio bots like Alexa, Siri, Google assistant, require a clean voice to perform a task. These bots ignore a disturbance or mixed voice. We get the famous message “sorry I could not understand”. With the introduction of smart homes and smart cities, it is imperative for devices to understand the commands in a noisy environment and that too in a native language. Indian Voice Separation and Enhancement (IVSE) offer the solution. For separation and enhancement of voice, the model should filter the noise first. For eliminating zero and negative values, a Zero-Negative filter (ZNF) is created. To eliminate the rippling effect induced by the time domain or frequency domain filters, Enhance Voice Function (EVF) enhances the voice. The output is windowed into different frames of equal lengths. Then it is labelled as noise and clean signals. A gradient boosting algorithm-based approach is then applied to filter the noise. The 50,000 voiceprints from the filtered voice were used to construct a training and validation set. For predictive analysis, the dataset is divided into an 80:20 ratio. IVSE uses LightGBM to create distinctive voiceprints. LightGBM operates in the background on Tensor Flow, which improves its performance. The paper compares IVSE and different benchmark algorithms at the end.

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Section: Reason For Choosing Lightgbmmentioning

confidence: 99%

IVSE: Indian Voice Separation and Enhancement from a cocktail party scenario

Gupta

Singh²,

Singh

2023

Preprint

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“…Morphologically, it is analytic language without the inflection of morphemes. Syntactically, it is usually the head-final language that the functional morphemes follow content morphemes, and the verb always becomes at the end of a sentence [17]. The sentences are delimited by a sentence boundary marker, but phrases and words are rarely delimited with spaces.…”

Section: Myanmar Languagementioning

confidence: 99%

“…We exploited RNN pre-ordering approach with lexicalized (Lex-RNN) and unlexicalized (Unlex-RNN) features. It took about 17 [22], was used for comparison. Hyper-parameters are set is being as: the matching features is the maximum value of 10, the window size is 3, and the maximum waiting time is set to 30 minutes.…”

Section: Training the Rnn Pre-ordering Modelmentioning

confidence: 99%

Source side pre-ordering using recurrent neural networks for English-Myanmar machine translation

Nyein

Soe

2021

IJECE

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Word reordering has remained one of the challenging problems for machine translation when translating between language pairs with different word orders e.g. English and Myanmar. Without reordering between these languages, a source sentence may be translated directly with similar word order and translation can not be meaningful. Myanmar is a subject-objectverb (SOV) language and an effective reordering is essential for translation. In this paper, we applied a pre-ordering approach using recurrent neural networks to pre-order words of the source Myanmar sentence into target English’s word order. This neural pre-ordering model is automatically derived from parallel word-aligned data with syntactic and lexical features based on dependency parse trees of the source sentences. This can generate arbitrary permutations that may be non-local on the sentence and can be combined into English-Myanmar machine translation. We exploited the model to reorder English sentences into Myanmar-like word order as a preprocessing stage for machine translation, obtaining improvements quality comparable to baseline rule-based pre-ordering approach on asian language treebank (ALT) corpus.

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“…Once the speech is separated the voice is not converted into text. For building robust acoustic models for speech recognition [68,69], accurate phonetic transcriptions are important. VoSE after enhancing the predicted voice converts the speech to text to make sure that the converted text matches the original speech's text.…”

Section: Why Lightgbm?mentioning

confidence: 99%

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

Gupta

Singh²,

Sinha³

2020

Preprint

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Voice Separation and Enhancement (VoSE) algorithm aims at designing a predictive model to solve the problem of speech enhancement and separation from a mixed signal. VoSE can be used for any language, with or without a large Datasets. VoSE can be utilized by any voice response system like, Siri, Alexa, Google Assistant which as of now work on single voice command. The pre-processing of the voice is done using a Trimming Negative and Nonzero voice filter (TNNVF), designed by the authors. TNNVF is independent of language, it works on any voice signal. The segmentation of a voice is generally carried out on frequency domain or time domain. Independently they are known to have ripple or rising effect. To rule out the ripple effect, data is filtered in the time-frequency domain. Voice print of the entire sound files is created for the training and testing purpose. 80% of the voice prints are used to train the network and 20% are kept for testing. The training set contains over 48,000 voice prints. LightGBM with TensorFlow helps in generating unique voice prints in a short time. To enhance the retrieved voice signals, Enhance Predictive Voice(EPV) function is designed. The tests are conducted on English and Indian languages. The proposed work is compared with K-means, Decision Stump, Naïve Bayes, and LSTM.

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Towards Burmese (Myanmar) Morphological Analysis

Cited by 20 publications

References 26 publications

IVSE: Indian Voice Separation and Enhancement from a cocktail party scenario

IVSE: Indian Voice Separation and Enhancement from a cocktail party scenario

Source side pre-ordering using recurrent neural networks for English-Myanmar machine translation

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

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