This was posted first on Skit's Tech blog here.
We often get asked about the differences between voice and chat bots. The most common perception is that the voice bot problem can be reduced to chat bot after plugging in an Automatic Speech Recognition (ASR) and Text to Speech (TTS) system. We believe that's an overly naive assumption about spoken conversations, even in restricted goal-oriented dialog systems. This post is an attempt to describe the differences involved and define what Speech-First Conversational AI means.
Speech is the most sophisticated behavior of the most complex organism in the known universe. - source
Conversational AI systems solve problems of conversations, either using text or voice. Since conversations are a common human behavior, there are many anthropomorphic expectations from these systems. These expectations, while still strong, are less restraining in text conversations as compared to speech. Speech is deeply ingrained in human communication and minor misses could lead to violation of user expectations. Contrast this with text messaging which is a, relatively new, human-constructed channel1 where expectations are different and more lenient.
There are multiple academic sources on differences between speech and text, here we will describing a few key differences that we have noticed while building speech-first conversation systems in more practical settings.
In addition to the textual content, speech signals contain information about the user's state, trait, and the environment. Speech isn't merely a redundant modality, but adds valuable extra information. Different style of uttering the same utterance can drastically change the meaning, something that's used a lot in human-human conversations.
Environmental factors like recording quality, background ambience, and audio events impact signals' reception and semantics. Even beyond the immediate environment, a lot of socio-cultural factors are embedded in speech beyond the level they are in text chats. Because the signals are rich, the difficulty of a few common problems across text and speech, like low-resource languages, is higher.
Once you go on transcribing audios utterances using ASRs, transcription errors will add on to your burden. While ASR systems are improving day-on-day, there still is error potential in handling acoustically similar utterances. Overall, an entirely new set of problems like far-field ASR, signal enhancement, etc. exist in spoken conversations.
Additionally many noisy deviations from fluent speech are not mere errors but develop their own pragmatic sense and convey strong meaning. Speech disfluencies are commonly assumed behaviors of natural conversations and lack of them could even cause discomfort.
3. Interaction Behavior
We don't take turns in a half-duplex manner while talking. Even then, most dialog management systems are designed like sequential turn-taking state machines where party A says something, then hands over control to party B, then takes back after B is done. The way we take turns in true spoken conversations is more full-duplex and that's where a lot of interesting conversational phenomenon happen.
While conversing, we freely barge-in, attempt corrections, and show other backchannel behaviors. When the other party also start doing the same and utilizing these both parties can have much more effective and grounded conversations.
Additionally, because of lack of a visual interface to keep the context, user recall around dialog history is different and that leads to different flow designs.
4. Personalization and adaptations
With all the extra added richness in the signals, the potential of personalization and adaptations goes up. A human talking to another human does many micro-adaptations including the choice-of-words (common with text conversations) and the acoustics of their voices based on the ongoing conversation.
Sometimes these adaptations get ossified and form sub-languages that need different approaches of designing conversations. In our experience, people talking to voice bots talks in a different sub-language, a relatively understudied phenomenon.
5. Response Generation
Similar to the section on input signals, the output signal from the voice bot is also (should also be) extremely rich. This puts a lot of stake in response production for natural conversation. The timing and content of sounds, along with their tones impart strong semantic and pragmatic sense to the utterance. Clever use of these also drive the conversations in a more fruitful direction for both parties.
Possibilities concerning this area of work is extremely limited in text messaging.
Finally, working with audios is more difficult than text because of additional and storage processing capabilities needed. Here is an audio utterance for the text "1 2 3":
❯ file counts.wav # 48.0 kB (48,044 bytes) counts.wav: RIFF (little-endian) data, WAVE audio, Microsoft PCM, 16 bit, mono 8000 Hz
Compare this with just 6 bytes needed for the string itself (
echo "1 2 3" | wc
These differences lead to gaps that are difficult to bridge and that's what keeps us busy at Skit. If these problems interest you, you should reach out to us on firstname.lastname@example.org.
Epistolary communication aside.