How does an AI Callbot work ?

The rise of voice technologies is revolutionising the way we interact, including in the field of customer service. Voice chatbots, equipped with increasingly sophisticated artificial intelligence (AI), have become key new players in customer relations. But how do they work? What are the inner workings of these virtual assistants, capable of conversing fluidly and naturally with users?

Speech recognition: transforming speech into text

The first important step in interacting with a YeldaAI voice chatbot is to convert speech into text. This stage, known as speech-to-text (STT) recognition, is based on complex algorithms capable of analysing the sound waves produced by the user and transcribing them into words that the computer can understand.

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The performance of speech recognition depends on a number of factors, such as the quality of the microphone, the level of ambient noise and the user's diction. Modern TTS systems incorporate machine learning techniques to adapt to the variability of language and constantly improve transcription accuracy.

Fundamental principle: natural language understanding

The core of a Voice Callbot is based on Natural Language Understanding (NLU), a branch of artificial intelligence dedicated to the interpretation of human language. Using advanced machine learning techniques, these systems are trained on huge corpora of speech data to discern linguistic nuances, implied intentions and the underlying semantic context.

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Acoustic analysis and conversion to text

When a user speaks to Callbot, their voice is first captured and digitised into an audio signal. This signal is then sent to a voice recognition module which, using acoustic analysis algorithms and statistical models, converts it into a textual representation that can be understood by the machine.

Capturing and digitising the speech signal

The first step is to capture the user's voice using a microphone. The microphone converts the sound waves into an analogue electrical signal. This signal is then digitised by an analogue-to-digital converter (ADC), which transforms it into a sequence of binary numbers that the computer can process.

Extraction of acoustic characteristics

The digitised speech signal is then analysed to extract relevant acoustic characteristics. These features, such as fundamental frequency, aspect ratio and formant, represent the physical properties of sound that enable the different phonemes (units of sound) in human speech to be distinguished.

Acoustic modelling and phoneme recognition

The acoustic characteristics extracted are then compared with vast databases of acoustic models. These models, built from annotated human speech corpora, enable the speech recognition system to identify the most likely phonemes corresponding to the input signal.

Language modelling and word breakdown

Simply recognising phonemes is not enough. The system must also understand the structure of the words and sentences in spoken language. This is where language modelling comes in. Statistical language models, based on text and speech corpora, enable the system to identify the most likely sequences of phonemes that form words and to break down the speech stream into distinct words.

Uncertainty management and error correction

Speech recognition is not a perfect process. Background noise, regional accents and individual variations in pronunciation can introduce errors into the transcription. To compensate for these imperfections, speech recognition systems incorporate uncertainty management and error correction techniques. These techniques enable the system to propose alternatives and choose the most plausible hypothesis based on the linguistic context.

Conversion to text and final output

Once the words have been identified, the system assembles them into a coherent sentence and produces a textual transcription of the user's utterance. This textual transcription is the input for the subsequent stages of natural language processing, enabling the voice chatbot to understand the user's intentions and provide an appropriate response.

Natural language processing: understanding intentions

Once speech has been transformed into text, the voice chatbot needs to understand its meaning. This is where automatic natural language processing (NLP) comes in. This technology enables the AI to analyse the text, identify the user's intentions, recognise the entities mentioned (names, places, dates, etc.) and grasp the nuances of language such as irony or sarcasm.

NLP uses statistical models and deep learning techniques to extract relevant information from the text and determine the next step in the conversation.

Speech synthesis: generating an audible response

To respond to the user, the voice chatbot must generate a response in the form of speech. This stage, known as text-to-speech (TTS), uses phoneme concatenation and prosody algorithms to transform written text into natural, fluid speech.

The quality of text-to-speech depends on the sophistication of the algorithms used and the amount of voice data available. Modern TTS systems are able to generate increasingly realistic and expressive voices, which contributes to a more pleasant user experience.

Continuous learning and improvement

Voice chatbots are constantly learning and improving by analysing past interactions with users. This self-learning capability enables STT, TALN and TTS algorithms to become more refined over time, improving their understanding of natural language and generating more relevant and personalised responses.

Conclusion

Voice chatbots, with their increasingly powerful artificial intelligence, are revolutionising customer interaction by offering a fluid and natural conversational experience.