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, and entirely bicycle-free talk on the same topic, try my ULAB 2018 keynote.
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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A quick introduction to a very simple but widely-applicable model that can perform classification (predicting a discrete label) or regression (predicting a continuous value). The tree is learned from labelled data, using supervised learning. Before watching this video, you might want to check that you understand what Entropy is.
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We make what is possibly the world’s simplest speech recognition system. It can only recognise two different words, but will help you understand the basic idea of pattern recognition using template matching. The templates are just pre-recorded words, with known labels. The features extracted are just two formant frequencies in the middle of the word, […]
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Just a toy demo, but should give you some idea of how unit selection waveform generation works. Click with your mouse to choose a candidate diphone from each column, then the corresponding synthesised waveform will appear. You can click on the synthesised waveform to hear it again. Try to obtain the most natural-sounding synthesis by […]
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The equation for the Gaussian probability density function looks a little scary at first, but this video should help you understand what each of the terms is doing, and how they fit together. After watching the video download the spreadsheet which shows the calculations and plots from this video (tip: the Apple Numbers.app version includes images […]
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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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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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In sampling and quantisation we saw that sampling a signal at a fixed rate means that there is an upper limit on the frequencies that can be represented. This limit is called the Nyquist frequency. Before sampling a signal, we must remove all energy above the Nyquist frequency, and here we will see what would […]
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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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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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Time-Domain Pitch Synchronous Overlap and Add (TD-PSOLA) can modify the fundamental frequency and duration of speech signals, without affecting the segment identity – that is, without changing the formants. Normally, it’s an automatic algorithm, but here we do it the hard way – by hand! If you want to follow-along, you will need Audacity and these materials (a […]
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