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Questions tagged [speech-recognition]

Automatic speech recognition (ASR) aims to identify words and phrases in spoken language and convert them to a machine-readable format.

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Where should i start to deal with language processing [closed]

what should i need to implement in order to process translation simultanously from one language to english in NLP python
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1answer
32 views

Speech recognition - hangover scheme - voice activity detection

I am doing a voice activity detection challenge, and I am asked to add a hangover scheme to the model. I read about hangover schemes in different papers but I couldn't find a definition for this. What ...
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1answer
171 views

What is low rank linear layer in neural networks?

Going through the paper Convolutional Neural Network for Small-footprint Keyword Spotting. In the paper, authors have used low rank linear layer after convolution ...
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1answer
30 views

Verifying Time Warp

Time warp has been widely assumed in domain of speech processing. If $Xw(t)$ represents a time warped version of $X(t)$, then $Xw(t) = X(t-w(t))$ where $w(t)$ is an arbitrary function with a banded ...
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39 views

How does CTC work in Speech Recognition?

I have already read the 2006 Paper about CTC by Graves but I still don't understand it fully. I am searching for a simple but still detailed explanation of how "Connectionist Temporal Classification" (...
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1answer
66 views

RNN(LSTM) model fails to classify new speaker voice

I'm fairly new to ML and at the moment I'm trying to develop a model that can classify spoken digits (0-9) by extracting mfcc features from audio files. My data ...
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1answer
165 views

Transfer learning for audio

I know that when working with images, what people normally do is download a big model trained with huge data and freeze most of the layers except the lasts ones to train them with their own data. I'm ...
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526 views

Validation loss is less than training loss by 5 units. How this result is interpreted?

Iam training a Keras model for end-to-end speech recognition. I have my own dataset of speech containing about 400 wave files. Text transcriptions is also given as input. Model summary is: ...
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1answer
50 views

Adapt speech recognition for Shakespeare english

We need to be able to search the works of Shakespeare by voice. The way I see it, the goal is if I quote into the microphone: "Yet but three come one more. Two of both kinds make up four. Ere ...
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1answer
2k views

Turkish speech recognition (speech->text) in Google Speech API? [closed]

Google's Speech API has audio speech to text capabilities in multiple languages. It supports Turkish too. That language is very interesting, it's so called agglutinative: you stick word parts one ...
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1answer
37 views

Word Error Rate over Data Set

In speech to text, one common metric is the word error rate (WER). WER is the word-level Levenshtein distance, which is the minimum number of substitutions ($S$), deletions ($D$), and insertions ($...
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1answer
218 views

How to normalize text when computing the word error rate of a speech recognition system?

I am looking for a library, script or program that can normalize the transcribed and gold texts when computing the word error rate (WER) of an automated speech recognition system. For example, if: ...
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1answer
52 views

Spot word in spoken sentence : advice needed

I have to make a live speech recognition program that can spot specific words in a spoken sentence. For now I have to recognize the words "yes" and "no". I already trained Google's model and it ...
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1answer
70 views

How to prevent the creation of redundant mixtures while training a GMM?

I'm currently trying to train a GMM(UBM) with 1024 Gaussian mixtures for speaker verification. However, after training the GMM, it appears that some mixtures are useless/redundant. (little to no ...
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0answers
339 views

Keyword Spotting: How to train a model with general speech corpus?

I am trying to find a correct way to train a DNN based keyword spotting (Deep KWS) with general speech corpus (VS data) described in this paper (Chen, Guoguo, Carolina Parada, and Georg Heigold. "...
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1answer
51 views

MFCCs and MoG-HMMs for speech recognition

BACKGROUND MFCCs are coefficients which represent the most important parts of speech, and about 12 of them are used to model a one 512 points long frame (of speech). Along with them you would use ...
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1answer
43 views

Why is phase reconstruction considered hard

I am studying deep learning models for single channel speech separation. I come across several recent methods: Permutation Invariant Training Deep Clustering Deep Attractor Network All of these ...
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2answers
285 views

Why has deep learning only shown decent results in the fields of computer vision and speech recognition? [closed]

We all know about the success of ImageNet, AlphaGo etc which used deep neural networks in computer vision, or the use of RNNs in Google Translate. But why are we not seeing similar advances in other ...
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0answers
94 views

Program to evaluate the output of a speech recognition system

I am looking for a library, script or program that can evaluate the output of a speech recognition system. The output of the speech recognition system is a simple text file, and I have the gold output ...
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1answer
72 views

how we input speech signal waveforms in two deep learning algorithms?

I am working with deep learning algorithms like CNN and RNN.I always wonder what is the best way to input wave form type data in to the deep learning algo. I know there are methods like wavelet or mel ...
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1answer
617 views

Forced alignment HMM

I am currently trying to understand what is involved to train a Hidden Markov Model (HMM) with Forced alignment. Forced alignment, as far I understand, is to align the audio file with the utterance ...
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1answer
380 views

Understanding hidden markov model, and how it is applied in speech recognition

I have for some some time tried to understand how this hidden markov model (hmm) works, and have found a lot of tutorials/papers on it which make use of the same examples/principles of explaining the ...
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1answer
664 views

How to derive the GMM log-likelihood formulation in the eigenvoice modeling technique?

Given a GMM with mean $M=[M_1, M_2, ..., M_C]$ and covariance $\Sigma=[\Sigma_1, \Sigma_2, ..., \Sigma_C]$ (where $C$ is the number of mixtures), many papers on eigenvoice modeling states that the ...