  Human conversational styles are measured by the sense of humor, personality,
and tone of voice. These characteristics have become essential for
conversational intelligent virtual assistants. However, most of the
state-of-the-art intelligent virtual assistants (IVAs) are failed to interpret
the affective semantics of human voices. This research proposes an
anthropomorphic intelligent system that can hold a proper human-like
conversation with emotion and personality. A voice style transfer method is
also proposed to map the attributes of a specific emotion. Initially, the
frequency domain data (Mel-Spectrogram) is created by converting the temporal
audio wave data, which comprises discrete patterns for audio features such as
notes, pitch, rhythm, and melody. A collateral CNN-Transformer-Encoder is used
to predict seven different affective states from voice. The voice is also fed
parallelly to the deep-speech, an RNN model that generates the text
transcription from the spectrogram. Then the transcripted text is transferred
to the multi-domain conversation agent using blended skill talk,
transformer-based retrieve-and-generate generation strategy, and beam-search
decoding, and an appropriate textual response is generated. The system learns
an invertible mapping of data to a latent space that can be manipulated and
generates a Mel-spectrogram frame based on previous Mel-spectrogram frames to
voice synthesize and style transfer. Finally, the waveform is generated using
WaveGlow from the spectrogram. The outcomes of the studies we conducted on
individual models were auspicious. Furthermore, users who interacted with the
system provided positive feedback, demonstrating the system's effectiveness.
