Token passing

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Token passing is a really nice way to understand (and even to implement) Viterbi search for Hidden Markov Models. Here we see token passing in action, and you can look at the spreadsheet to see the calculations.

To keep things simple, we are ignoring transition probabilities in this example. It would be simple to add them – tokens would just multiply their likelihood by the transition probability every time they went down an arc.

To learn more, read this tech report.

Autocorrelation for estimating F0

Autocorrelation

Most methods for estimating F0 start from autocorrelation. The idea is pretty simple: we are just looking for a repeating pattern in the waveform, which corresponds to the periodic vocal fold activity. For some waveforms, it might be possible to do that directly in the time domain, but in general that doesn’t work very well. […]

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Wave propagation on the surface of water

At the Alhambra (Granada, Spain) I saw this nice example of waves from a point source propagating in all directions at a fixed speed.

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A simple synthetic vowel

Using Praat, we synthesise a simple vowel-like sound, starting with a pulse train, which we pass through a filter with resonant peaks.

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Pipeline architecture for TTS

Pipeline architecture

Most text-to-speech systems split the problem into two main stages. The first stage is called the front end and contains many separate processes which gradually build up a linguistic specification from the input text. The second stage typically uses language-independent techniques (although they still require a language-specific speech corpus) to generate a waveform. Here we see those two […]

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Sampling and quantisation

Is digital better than analogue? Here we discover that there are limitations when storing waveforms digitally. We learn that the consequence of sampling at a fixed rate is an upper limit on the frequencies that can be represented, called the Nyquist frequency. In addition to the limitations of sampling, storing each sample of the waveform as a […]

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Spectrum and spectrogram

The spectrum and the spectrogram are much more useful ways of analysing speech signals than the waveform. We look at how to create them using Wavesurfer and what effect the analysis window size has on what we see.    

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Entropy: understanding the equation

The equation for entropy is very often presented in textbooks without much explanation, other than to say it has the desired properties. Here, I attempt an informal derivation of the equation starting from uniform probability distributions. A good way to think about information is in terms of sending messages. In the video, we send messages […]

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Windowing

When we say that a signal is non-stationary we mean that its properties, such as the spectrum, change over time. To analyse signals like this, we need to first assume that these properties do not change over some short period of time, called the frame. We can then analyse individual frames of the signal, one at a […]

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My inaugural lecture

I talk about how speech synthesis works, in what I hope is a non-technical and accessible way, and finish off with an application of speech synthesis that gives personalised voices to people who are losing the ability to speak. I also try to mention bicycles as many times as possible. For a more up-to-date, slightly more technical, […]

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Bitrate

The bitrate (or bit rate) of a signal is the number of bits required to store, or transmit, 1 s of that signal. A bit is a binary number: either 0 or 1. Let’s calculate the bitrate of a digital waveform. First you should revise the concepts of sampling and quantisation from this module of the […]

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The speed of sound

At the Parque de las Ciencias in Granada, Spain there is this long tube, open at the end nearest you and closed at the far end. We can calculate the length of this tube just from the audio recording, because we know the speed of sound. Here’s the waveform of part of the recording, showing […]

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