We fail to identify S a accurately due to the overlapping spectral content of the same gender speakers, especially in the lower frequency band.
The existence of different contents, genders, speakers, and speaking styles raise complications because these properties have direct affect on the features such as pitch, and energy contours [2].
For this figure, an additional masking threshold was introduced, and the mask in (11) was only set to 1, if the source of interest was greater than all other sources by at least dB, that is, if (12). Figure 1 Performance of an ideal binary mask, tested on 12 pairs of same-and mixed-gender speakers.
Dental nonsibilants and velar obstruents showed more complex distributions involving both gender and speaker dialect.
In the first two columns of the Table 8, the experimental results with acoustic models adapted to the male and female gender of speaker evaluated on the whole test data set are presented.
In our experiments, and by considering the gender of speakers, as it can be inferred from Table 5 (without a LM, i.e., experiments that numbered by subscript ), we can notice that female speakers give better system overall accuracy.
Speech database consists of three subsets (see the Table 1): The first part is characterized by gender-balanced speakers, contains 250 h of speech recordings obtained from 250 speakers together and consists of two parts: APD1 and APD2 databases.
The first part is characterized by gender-balanced speakers, contains 250 h of speech recordings obtained from 250 speakers together and consists of two parts: APD1 and APD2 databases.
My colleagues and I noted the gender of the speaker and audience (listener), whether the speaker or the audience laughed, and what was said immediately before laughter occurred.
After hearing each sentence, participants were oriented towards the gender of the speaker by being asked to judge whether the sentence had been read aloud by a male or female speaker.
