Sex-specific fundamental and formant frequency patterns in a cross-sectional study

An extensive developmental acoustic study of the speech patterns of children and adults was reported by Lee and colleagues [Lee et al., J. Acoust. Soc. Am. 105, 1455-1468 (1999)]. This paper presents a reexamination of selected fundamental frequency and formant frequency data presented in their report for 10 monophthongs by investigating sex-specific and developmental patterns using two different approaches. The first of these includes the investigation of age- and sex-specific formant frequency patterns in the monophthongs. The second, the investigation of fundamental frequency and formant frequency data using the critical band rate (bark) scale and a number of acoustic-phonetic dimensions of the monophthongs from an age- and sex-specific perspective. These acoustic-phonetic dimensions include: vowel spaces and distances from speaker centroids; frequency differences between the formant frequencies of males and females; vowel openness/closeness and frontness/backness; the degree of vocal effort; and formant frequency ranges. Both approaches reveal both age- and sex-specific development patterns which also appear to be dependent on whether vowels are peripheral or non-peripheral. The developmental emergence of these sex-specific differences are discussed with reference to anatomical, physiological, sociophonetic and culturally determined factors. Some directions for further investigation into the age-linked sex differences in speech across the lifespan are also proposed

Blind Normalization of Speech From Different Channels

We show how to construct a channel-independent representation of speech that has propagated through a noisy reverberant channel. This is done by blindly rescaling the cepstral time series by a non-linear function, with the form of this scale function being determined by previously encountered cepstra from that channel. The rescaled form of the time series is an invariant property of it in the following sense: it is unaffected if the time series is transformed by any time-independent invertible distortion. Because a linear channel with stationary noise and impulse response transforms cepstra in this way, the new technique can be used to remove the channel dependence of a cepstral time series. In experiments, the method achieved greater channel-independence than cepstral mean normalization, and it was comparable to the combination of cepstral mean normalization and spectral subtraction, despite the fact that no measurements of channel noise or reverberations were required (unlike spectral subtraction).Comment: 25 pages, 7 figure

On Using Backpropagation for Speech Texture Generation and Voice Conversion

Inspired by recent work on neural network image generation which rely on backpropagation towards the network inputs, we present a proof-of-concept system for speech texture synthesis and voice conversion based on two mechanisms: approximate inversion of the representation learned by a speech recognition neural network, and on matching statistics of neuron activations between different source and target utterances. Similar to image texture synthesis and neural style transfer, the system works by optimizing a cost function with respect to the input waveform samples. To this end we use a differentiable mel-filterbank feature extraction pipeline and train a convolutional CTC speech recognition network. Our system is able to extract speaker characteristics from very limited amounts of target speaker data, as little as a few seconds, and can be used to generate realistic speech babble or reconstruct an utterance in a different voice.Comment: Accepted to ICASSP 201