Sensory Systems/Human Speech

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Human Speech[edit]



The intensity of sound is typically expressed in deciBel (dB), defined as

 SPL = 20 * log \frac{p}{p_0}

where SPL = “sound pressure level” (in dB), and the reference pressure is p_0 = 2*10^{-5} N/m^2 . Note that this is much smaller than the air pressure (ca. 105 N/m2)! Also watch out, because sound is often expressed relative to "Hearing Level" instead of SPL.

  • 0 - 20 dB SPL ... hearing level (0 dB for sinusoidal tones, from 1 kHz – 4 kHz)
  • 60 dB SPL ... medium loud tone, conversational speech

Fundamental frequency, from the vibrations of the vocal cords in the larynx, is about 120 Hz for adult male, 250 Hz for adult female, and up to 400 Hz for children.

Frequency- and loudness-dependence of human hearing loss.


Formants are the dominant frequencies in human speech, and are caused by resonances of the signals from the vocal cord in our mouth etc. Formants show up as distinct peaks of energy in the sound's frequency spectrum. They are numbered in ascending order starting with the format at the lowest frequency.

Spectrogram of the German vowels "a,e,i,o,u". These correspond approximately to the vowels in the English words "hut, hat, hit, hot, put". Calculated using the MATLAB command "spectrogram(data, 512,256, 512, fs)". The chapter Power Spectrum of Non-stationary Signals below describes the mathematics behind the spectrogram.


Speech is often considered to consist of a sequence of acoustic units called phons, which correspond to linguistic units called phonemes. Phonemes are the smallest units of sound that allows different words to be distinguished. The word "dog", for example, contains three phonemes. Changes to the first, second, and third phoneme respectively produce the words "log", "dig", and "dot". English is said to contain 40 different phonemes, specified as in /d/, /o/, /g/ for the word "dog".

Speech Perception[edit]

The ability of humans to decode speech signals still easily exceeds that of any algorithm developed so far. While automatic speech recognition has become fairly successful in recognizing clearly spoken speech in environments with high Signal-to-noise ratio, once the conditions become a bit less than ideal, recognition algorithms tend to perform vary poorly compared to humans. It seems from this that our computer speech recognition algorithms have not yet come close to capturing the underlying algorithm that humans use to recognize speech.

Evidence has shown that the perception of speech takes quite a different route than the perception of other sounds in the brain. While studies on non-speech sound responses have generally found response to be graded with stimulus, speech studies have repeatedly found a discretization of response when a graded stimulus is presented. For instance, Lisker and Abramson,[1] played a pre-voiced 'b/p' sound. Whether the sound is interpreted as a /b/ or a /p/ depends on the voice onset time (VOT). They found that when smoothly varying the VOT, there was a sharp change (at ~20ms after the consonant is played) where subjects switched their identification from /b/ to /p/. Furthermore, subjects had a great deal of difficulty differentiating between two sounds in the same category (e.g. pairs of sounds with a VOTs of -10ms to 10m, which would both be /b/'s, than sounds with a 10ms to 30ms, which would be identified as a b and a p). This shows that some type of categorization scheme is going on. One of the main problems encountered when trying to build a model of speech perception is the so-called 'Lack of Invariance', which could more straightforwardly just be stated as the 'variance'. This term refers to the fact that a single phoneme (e.g. /p/ as in sPeech or Piety), has a great variety of waveforms that map to it, and that the mapping between an acoustic waveform and a phoneme is far from obvious and heavily context-dependent, yet human listeners reliably give the correct result. Even when the context is similar, a waveform will show a great deal of variance due to factors such as the pace of speech, the identity of the speaker and the tone in which he is speaking. So while there is no agreed-upon model of speech perception, the existing models can be split into two classes: Passive Perception and Active perception.

Passive Perception Models[edit]

Passive perception theories generally describe the problem of speech perception in the same way that most sensory signal-processing algorithms do: Some raw input signal goes in, and is processed though a hierarchy where each subsequent step extracts some increasingly abstract signal from the input. One of the early examples of a passive model was distinctive feature theory. The idea is to identify the presence of sets of binary values for certain features. For example, 'nasal/oral', 'vocalic/non-vocalic'. The theory is that a phoneme is interpreted as a binary vector of the presence or absence of these features. These features can be extracted from the spectrogram data. Other passive models, such as those described by Selfridge[2] and Uttley,[3] involve a kind of template-matching, where a hierarchy of processing layers extract features that are increasingly abstract and invariant to certain irrelevant features (such as identity of the speaker when classifying phonemes).

Active Perception Models[edit]

An entirely different take on speech perception are active-perception theories. These theories make the point that it would be redundant for the brain to have two parallel systems for speech perception and speech production, given that the ability produce a sound is so closely tied with the ability to identify it - proponents of these theories argue that it would be wasteful and complicated to maintain two separate databases-one containing the programs to identify phonemes, and another to produce them. They argue that speech perception is actually done by attempting to replicate the incoming signal, and thus using the same circuits for phoneme production as for identification. The Motor Theory of speech perception (Liberman et al., 1967), states that speech sounds are identified not by any sort of template matching, but by using the speech-generating mechanisms to try and regenerate a copy of the speech signal. It states that phonemes should not be seen as hidden signals within the speech, but as “cues” that the generating mechanism attempts to reproduce in a pre-speech signal. The theory states that speech-generating regions of the brain learn which speech-precursor signals will produce which sounds by the constant feedback loop of always hearing one's own speech. The babbling of babies, it is argued, is a way of learning this how to generate these “cue” sounds from pre-motor signals.[4]

A similar idea is proposed in the analysis-by-synthesis model, by Stevens and Halle.[5] This describes a generative model which attempts to regenerate a similar signal to the incoming sound. It essentially takes advantage of the fact that speech-generating mechanisms are similar between people, and that the characteristic features that one hears in speech can be reproduced by the speaker. As the speaker hears the sound, the speech centers attempt to generate the signal that's coming in. Comparators give constant feedback on the quality of the regeneration. The 'units of perception', are therefore not so much abstractions of the incoming sound, as pre-motor commands for generating the same speech.

Motor theories took a serious hit when a series of studies on what is now known as Broca's Aphasia were published. This condition impairs one's ability to produce speech sounds, without impairing the ability to comprehend them, whereas motor theory, taken in its original form, states that production and comprehension are done by the same circuits, so impaired speech production should imply impaired speech comprehension. The existence of Broca's aphasia appears to contradicts this prediction.[6]

Current Models[edit]

The TRACE model of speech perception. All connections beyond the input layer are bidirectional. Each unit represents some unit of speech such as a word of a phoneme.

One of the most influential computational models of speech perception is called TRACE.[7] TRACE is a neural-network-like model, with three layers and a recurrent connection scheme. The first layer extracts features from an input spectrogram in temporal order, basically simulating the cochlea. The second layer extracts phonemes from the feature information, and the third layer extracts words from the phoneme information. The model contains feed-forward (bottom-up) excitatory connections, lateral inhibitory connections, and feedback (top-down) excitatory connections. In this model, each computational unit corresponds to some unit of perception (e.g. the phoneme /p/ or the word "preposterous"). The basic idea is that, based on their input, units within a layer will compete to have the strongest output. The lateral inhibitory connections result in a sort of winner-takes-all circuit, in which the unit with the strongest input will inhibit its neighbors and become the clear winner. The feedback connections allow us to explain the effect of context-dependent comprehension - for example, suppose the phoneme layer, based on its bottom-up inputs, could not decide whether it had heard a /g/ or a /k/, but that the phoneme was preceded by 'an', and followed by 'ry'. Both the /g/ and /k/ units would initially be equally activated, sending inputs up to the word level, which would already contain excited units corresponding to words such as 'anaconda', 'angry', and 'ankle', which had been activated by the preceding 'an'. The excitement of the /g/ or /k/


  1. Lisker, L.; Abramson (1970). "The voicing dimension: Some experiments in comparative phonetics". in B. Hála, M. Romportl and P. Janota. Proceedings of the 6th International Congress of Phonetic Sciences. Prague: Academia. 
  2. Selfridge, O.C (1959) "Pandemonium: a paradigm for learning". in Proceedings of the Symposium on Mechanisation of Thought Process. National Physics Laboratory.
  3. Uttley, A.M. (July 1966). "The transmission of information and the effect of local feedback in theoretical and neural networks". Brain Research 2 (1): 21–50. doi:10.1016/0006-8993(66)90060-6. 
  4. Liberman, A. M.; Mattingly, I. G.; Turvey (1967). "Language codes and memory codes". in Melton, A. W.; Martin, E.. Coding Processes in Human Memory. V. H. Winston & Sons. pp. 307-334. 
  5. Stevens, K. N.; Halle, M. (1967). "Remarks on analysis by synthesis and distinctive features". in Wathen-Dunn, W.. Models for the perception of speech and visual form: proceedings of a symposium. Cambridge, MA: MIT Press. pp. 88–102. 
  6. Hickok, Gregory (January 2010). "The role of mirror neurons in speech and language processing". Brain and Language 112 (1): 1–2. doi:10.1016/j.bandl.2009.10.006. 
  7. McClelland, James L; Elman, Jeffrey L (January 1986). "The TRACE model of speech perception". Cognitive Psychology 18 (1): 1–86. doi:10.1016/0010-0285(86)90015-0.