Audition 3

Three pairs of matrix sentences, recorded from a single male Australian English talker, were presented from the three loudspeaker locations (Figure 1). Matrix sentences were syntactically fixed and comprised of name, verb, number, adjective, and noun elements. Sentences were constructed at each trial by randomly sampling each element without replacement from a list of 10 possible words. All words within a trial occurred only once, with the exception of the target name which occurred twice.
Words were 500 ms in duration with the exception of nouns, which were time stretched to 600 ms using Adobe Audition 3.0. This manipulation was applied to reproduce the natural prosodic lengthening of speech at phrase boundaries (Wightman et al., 1992 (link)). A 350 ms silence gap was introduced between sentence pairs to replicate the average conversational turn-taking duration of English speech (Stivers et al., 2009 (link)). In addition, sentences were staggered with a 50 ms offset to (i) reduce the effects of energetic masking encountered with synchronized concurrent talkers and (ii) enhance grouping by staggering onsets. Offset combinations were randomized each trial and balanced for all locations. Stimuli were generated using Matlab (MathWorks) and played through an RME FireFace UCX soundcard at 48 kHz sampling rate. All sentences were presented at 65 dB SPL.

Uncompressed audio files were extracted from videos and low-pass filtered at 0.5 kHz using a Butterworth filter (24 dB/octave roll off, 100 Hz transition bandwidth) in Adobe Audition 3.0. Following the filtering process, files were amplitude normalized. This level of filtering reduces lexical identification to near-zero while retaining energy at the fundamental frequency (i.e., the acoustic correlate of perceived pitch), the first formant (i.e., speech frequency band that contributes to phonetic judgments), as well as amplitude and speech rhythm dynamics [45 (link), 46 ].

The sound source recorded in the previous study was used (Asaba et al., 2015 (link); Nomoto et al., 2020 (link)). One male BALB/c mouse was used for recording USVs (purchased from CLEA Japan, Inc., Tokyo, Japan). To promote vocalizations, in a sound-attenuated chamber, the male mouse was paired with a female C57BL/6 mouse, which was ovariectomized and devocalized. Sexual receptivity of the female mouse was hormonally induced. A microphone (CM16/CMPA, Avisoft Bioacoustics, Glienicke/Nordbahn, Germany) capable of recording USVs was placed through the wire mesh of the cage. The USVs were digitally converted, filtered using a 20–145.8 kHz band-pass filter, and recorded at a sampling rate of 300 kHz (UltraSoundGate 116H and RECORDER USGH, Avisoft Bioacoustics, Glienicke/Nordbahn, Germany). A fragment of 20 s was extracted from the original sound file (Audition 3.0, Adobe, CA, USA). Ambient noises were digitally reduced. A silent part of the sound file was used as background noise.

Lexical tones and pure tones were used as stimuli in this study. Lexical-tone stimuli were obtained and slightly modified from those used in our previous study (Luo et al., 2006 (link)), in which the Mandarin consonant-vowel (CV) syllables /bai1/ and /bai4/ were employed and originally pronounced by an adult male Mandarin speaker (Sinica Corpus, Institute of Linguistics, Chinese Academy of Social Sciences, Beijing, China). Pure tones were generated by Audition 3.0 (Adobe Systems Inc., Mountain View, CA, USA). The duration of each lexical tone was normalized to 350 ms, the duration of each pure tone was 200 ms, and both included a 5-ms linear rise and fall time. The lexical-tone contrast was created by a sequence of /bai1/ frequently presented as the standard stimuli and /bai4/ infrequently presented as the deviant stimuli during the auditory stream. The pure-tone contrast was created by a sequence of pure tones frequently presented at 550 Hz as the standard stimuli and infrequently presented at 350 Hz as the deviant stimuli.

The sounds have been taken from the databank of our laboratory. All sounds had an equal duration (800 ms). Sounds were normalized in peak intensity with Adobe Audition 3.0 with a 2 ms fading-in and sound intensity was measured with a sonometer (Roline, RO-1350).