RNN or CNN, that’s the question. so what should you use?

Let the battle begin

So speech recognition is a very broad task. People use speech recognition to do speech-to-text on videos, a pre-recorded data which you can go back and forth between past and future and optimize the output a couple of times. On the other hand voice control is also a speech recognition task, but you need to do all this speech processing in real time. And within a low latency time manner.

And now comes the big question. Which technologies should you use RNN or CNN? in this post, We’re going to talk about that

so generally, speech waveform data by using a CMVN or MFCC, can be converted to 2D image data and then, from that point is basically an image that you can show to people and people can learn how each word will look like. So, it is basically detecting where exactly the word is happening. And it’s very similar to an object detection task. very similar, but not quite the same. And why is that? So, a lot of times we also have trouble detecting words but we are using the language grammar in the background of our head to predict what exactly the next word would look like. And we’re using that and combining that with the waveform data and then we detect the right word so if you say, a very strange word to people, they will have trouble getting the correct text out of it. But if you teach them a couple of times, and they know that when these words pop up, they will have much less trouble detecting them. So the machine learning community uses the same approach.

In modern speech recognition engines ML engineers first use CNN to capture the features, or at least detect how likely the word is, and then run an RNN in the background as a language model to improve the result. So in the case of voice control, you don’t care about the language model, because there is no language model. You can say whatever word you want, or at least we give you that freedom, and then you just need the text out of the word that you just said.

So in that case there is no use for RNN as there is no language model. And a 1D convolutional network is enough. So it is the same as localization and object detection in classic machine learning. So if you use the same technique as YOLO to move around the convolution layer, around the waveform, and just detect the maximum confidence score on a window, then you can find the exact word happening at that time. The problem is as the number of words increases and increases this technique will become more and more challenging. So you need to develop more mature techniques. And that’s exactly why we introduced HMM. The best technique is to use a hidden Markov model To detect which word is spoken in a certain way and then slide that word over the signal and find out if it’s actually that signal or not. And by using that we can do that alignment. We can do better force alignment, use that data, and also feed it to Hmm, To increase the accuracy and finally, we create this awesome engine with a great amount of accuracy that no one has seen ever before.

So wait for it and Sleep on it.

• Batch Size: There is no such a thing as batch size, there’s only mini batch size which is number of the samples you train your model on before updating the weight and biases but sometimes programmers just drop the mini in the term, and use batch sizes instead so basically batch size is exactly the same as the minibatch size. If we want to be absolutely correct though then the batch size is the number of samples we have in our data set and it’s not a number that you can specify in your program
• MSE: Mean square root error will work best if you want to calculate the error for predicting a function regression, like predicting stock price, or a product lifetime failure function. In fact, there is no mean in MSE algorithm And the error is calculated between the neural network output and the prediction label. and then summation over all different samples.
• Cross Entropy: Cross Entropy will work best if you want to do a classification like binary classification or even a multi-class classification. It helps by penalizing wrong class detection. The calculation of the cross-entropy function is fairly easy it is -log(p_target_class). So basically it only depends on the class that the input data is belonged to. In this case other classes error don’t have any impact on the network loss.
• ArgMax: ArgMax is a function that is used at the final output layer of the neural network. It simply changes the class with the maximum number to one and all other cases to zero

slavv – 37 reasons why your neural network-is-not-working

Intel – TBB

To calculate word level confidence score Kaldi uses a method called MBR Decoding. MBR Decoding is a decoding process that minimize word level error rate (instead of minimizing the whole utterance cost) to calculate the result. This may not give the accurate result but can be use to calculate the confidence score up to some level. Just don’t expect too much as the performance is not well-accurate.

Here are some key concepts:

1. Levenshtein Distance: Levenshtein Distance or Edit Distance compute difference between two sentences. It computes how many words are different between the two. Lets say X and Y are two word sequence shown below. The Levenshtein distance would be 3 where Ɛ represent empty word

To calculate the Levenshtein distance you can use following recursive algorithm where A and B are word sequence with length of N+1

As in all recursive algorithm to decrease amount of duplicate computation Kaldi used the memoization technique and store the above three circumstances in a1, a2 and a3 respectively

2. Forward-Backward Algorithm: Lets say you want to calculate the probability of seeing a waveform(or MFCC features) given a path in a lattice (or on HHM FST). Then the Forward-Backward Algorithm is nothing more than a optimized way to compute this probability.

3. Gamma Calculation: TBA

4. MBR Decoding: TBA

Delta-Delta feature is proposed in 1986 by S. Furui and Hermann Ney in 1990. It’s simply add first and second derivative of cepstrum to the feature vector. By doing that they say it can capture spectral dynamics and improve overall accuracy.

The only problem is that in a discrete signal space getting derivative from the signal increase spontaneous noise level so instead of simple first and second order derivative HTK proposed a differentiation filter. This filter basically is a convoluted low-pass filter on top of discrete signal derivative to smooth out the result and remove unwanted noises. In Fig 1 you can see the result of simple second derivative vs the proposed differentiation filter.

Fig. 1. Plain dervative VS differentiation filter. courtesy of Matlab™

HTK filter for a Delta-Delta feature (order=2, window=2) is a 9 element FIR filter with following coefficient(Θ is window size which is 2 in HTK)

• I’m thinking about using using compressor filter in speech instead of CMVN to normalize in real-time.

• The above formula c_t makes no sense. I will update this as soon as I can post codes here

IEEE ICASSP ’86 – Isolated Word Recognition Based on Emphasized Spectral Dynamics

IEEE ICASSP ’90 – Experiments on mixture-density phoneme-modelling for 1000-word DARPA task

Desh Raj Blog – Award-winning classic papers in ML and NLP

Lattice Decoder In A Glance

Fig. 1. After First Emitting Nodes Process

Fig. 2. After Second Emitting Nodes Process

A Simplified Block Diagram of ASR Process in Kaldi

1. NGC Nvidia – Kaldi Container
2. Oxinabox – Kaldi Notes
3. KWS14 – Kaldi Lattices

Fig. 1. Demonstration of Finite State Automata vs Lattices, Courtesy of Peter F. Brown

1. Stanford University – Speech and Language Processing Book
2. IEEE ICASSP – Partial traceback and dynamic programming