LPC voice coders (starting with the old LPC10 standard, which seems to be the one you refer to here) are based on the source-filter model of speech production. Speech can be characterized by the following properties:
The raw sound emitted by the larynx (through vibration of the vocal folds, or just air flowing through it, the vocal folds being opened).
In my answer to your that question, I had mentioned that Voice Activity Detection is a standard feature for codecs like G.729 and such others.
You should look for reference encoders and decoders for algorithms that applies this.
One such example is - http://www.voiceage.com/openinit_g729.php
Another possible source is Speex codec. Which implements VAD
This is a well-studied problem, dating back from the mid 90s (DARPA/NIST broadcast transcription challenges). Search for "speech/music segmentation" or "audio segmentation" and you'll find thousands of research papers.
There are two broad approaches to solve this problem:
Train a speech/music classifier, using a standard machine ...
The first equation you give is the difference equation for a lowpass FIR filter, or a linear filter with an impulse response that is finite in duration. I'll write it a bit differently (so that it is expressly discrete in time and causal):
f_s[n] = 0.1 f[n-2] + 0.8 f[n-1] + 0.1 f[n]
$f_s[n]$ is the smoothed version of the discrete-time input sequence ...
The energy of a discrete-time signal is the sum of the squares:
E_x = \sum_n |x[n]|^2
If you need the average power of your signal, divide the energy by the number of samples in your vector. Power is the average energy per sample of your signal.
If your signal vector is a sample of a stationary process (i.e. a recording clip of someone talking), ...
Now, I would like to show what frequencies the speech has. However,
I'm not sure what would be the best way to do that. It seems sometimes
one calculates the absolute value of a Fourier transform, and
sometimes power spectral density.
If you want to attach physical meaning to your analysis, then go with the power spectral density, (PSD). This is ...
A few notes regarding your approach and detailed questions:
First, it is very common in audio analysis to split the signal to be analyzed into short overlapping windows, because audio signals are not stationary (their characteristics change over time), so they need to be processed on short segments over which they can be considered stationary (many analyses ...
Unless you have tight space constraints, I would stick to lossless formats like good old WAV. Phones do not pass much high-frequency content so you should be fine with a 8KHz Mono WAVE file. You will however want to keep the bit depth high, say to 24 bits, since you want to process the audio. Once you are finished with your processing you can compress with a ...
A recording originally at 8kHz and digitally upsampled to 16kHz will have almost no energy in the 4-8kHz range (whatever is here is due to imperfections in the filters used for the upsampling process). I would just use a 4kHz and 5.5kHz high pass; and use a threshold on the signal energy at the output of these filters.
... Unless your recordings are ...
There are open source implementations in the Sphinx and Freeswitch projects. I think they are all energy based detectors do won't need any kind model.
Sphinx 4 (Java but it should be easy to port to C/C++)
Answer taken from Stackoverflow question.
Researchers from the Johns Hopkins University have recently released a corpus of music, speech, and noise which, according to them, is suitable for training models for voice activity detection and music/speech discrimination.
See https://arxiv.org/pdf/1510.08484.pdf for details.
HMM are useful for sequence modeling and classification - problems for which your observations unfold on a 1-D axis in time or space. Hence their usefulness for speech recognition, because a word is a sequence of heterogeneous states corresponding to its various phones. But the problem of recognizing whether a speaker is male or female doesn't really have ...
The amount of knowledge necessary to develop such a large scale multi-language, speaker-independent, large-vocabulary speech recognition system is spread well over hundreds of papers; and each individual brick of the system (say the feature extraction front-end, the FST decoding library, the language model store) is developed by world-class expert in this ...
Depending on the actual recordings, the algorithm complexity could range from dead easy to really complex...
I'll take the studio recording case first, so I can assume :
- (Almost) no noise coming from outside (cars, trucks, bus...)
- Nobody slamming the door in the middle of the recording
- Voice samples are recorded at optimal level independently of who ...
Have a look at the acoustic scale website for some inspiration
You can estimate age based on a combination of vocal tract length (vtl) and pitch. Both of these attributes can be extracted from vowel sounds. Children will have short vtls and high pitch, adult males will have low pitch and long vtl, adult ...
Voice Activity Detection using Adaptive Threshold is very easy and handy to implement on any platform
Here you can have a algorithm which is Adaptive Energy based
Small addition to above algorithm when you are calculating for very first time go for taking Mean of Energy and mark as Emin
in this the frame you pass is divided in to sub-frames and further ...
I learnt to use HTK based on this very basic tutorial
It details how to make a yes/no recogniser. It should get you off to a good start at least. There is also some HTK code on the main HTK site that allows you to perform real time speech recognition.
To improve robustness, ...
Zero-crossing rate (ZCR) might be useful for voiced/unvoiced frame discrimination, speech/music discrimination, but it is of much lesser importance in speech recognition. One reason is that it is pitch-dependent and not robust to background noise or hum. It is not difficult to craft very different signals (say a female voice saying a phone and a male voice ...
Assume noise is not a serious issue in your problem. I guess you can get pretty clear speech signal. If you have speech recognition part implemented in your system, I think you should be able to take advantage over the language model in your recognition system. According to the transition probabilities, you shall get some confidence to say at what moment ...
White noise implies no correlation between samples of the noise, even consecutive samples. Colored noise, therefore, implies that there is correlation of some sort between the noise samples, which in turn implies that we can take advantage of that correlation to get rid of some of the noise.
Beyond that, there is not a lot that we can say about what it ...
It's better to copy first frame and last frame values to extend vector sequence beyond boundaries than to assign 0. This could be implemented just by adjusting indexes:
if (index1 < 0)
index1 = 0
if (index2 > N - 1)
index2 = N - 1
delta = v[index1] - v[index2]
You can take some big speech corpus like TEDLIUM and add the noise you like:
The advantage of TEDLIUM is that it's a set of continuous recordings with speech timings, not just a collection of utterances.
Dynamic Time Warping is pretty well explained on this site. I'll use some of the diagrams from the PPT on that site to explain.
The idea is to divide the signals into segments (frames) and then compare frames sequentially through each signal. As illustrated below, motion from a segment in one signal to the next segment depends on the similarity to the ...
What you are observing is the digital representation of the voltage, which in fact represents the acoustic pressure.
Workflow would be something like:
Vibrating larynx is producing Acoustic Pressure [Pa]
Variations of that pressure are converted by the microphone into Voltage [V]
Voltage is being sampled and quantized by the ADC (Analog to Digital Converter)...
Yes, the cellular phones use various forms of compression to convert the captured analog audio (speech) into the digital bitstream for transmission through 2G/3G/.../.
The specific method used depends on the GSM version which might dictate its own bandwidth constraint and backward or forward compatibility issues into the audio encoding stage.
There is no best pdf for a periodic signal. There is also no way to find the 'exact' pdf of a measured signal. What you have to do is to measure the pdf from the data. Use a histogram to approximate the pdf of your data. Define a number of intervals within the amplitude range of your signal and simply count the numbers of data samples per interval. This ...
I completely agree with Jim Clay's answer; Speech Recognition is hard, and typically requires years of study to do with any kind of accuracy. To guide you toward a more modern approach than that webpage you have listed in your comments, however, the typical method of doing something like this is to transform the chunks of size N to the Mel-frequency cepstrum ...
Ok, if my data sample is 4410 then how could I possibly split that into equal size blocks?
I wouldn't get too fixated on that. You could just break it up into chunks of size $N$, and then have an odd-sized block at the end that is whatever is left over.
I am not a speech processing expert by any means, but this doesn't seem like the best way to go about ...
The important thing to understand about something like a speech signal is that its frequency components are time-varying. In order to represent speech in the frequency domain we usually take a short enough window of the signal within which we can assume that the spectrum of the speech does not vary significantly (typically 10 ms). So we calculate the power ...