Exam AI-102 topic 1 question 23 discussion

It should be: - Speech to Text with AutoDetectSourceLanguageConfig. It can't be Text Analytics because the input is callers' voice. - Text to Speech: the output is voice.

Speech-to-Text : https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/speech-to-text Text-to-Speech : https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/text-to-speech Both support common languages, including French. https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/language-support?tabs=speechtotext

Detect the incoming language - Speech to Text because it includes the capability to detect the language of the spoken content using the AutoDetectSourceLanguageConfig configuration. This is suitable for identifying whether the incoming language is French or English, which is essential for routing the call to the appropriate language handling system. Respond in the caller's own language - Text to Speech because it converts text into spoken language, which allows the system to respond to callers in their own language, either French or English

1. Speech to Text 2. Text to Speech

https://learn.microsoft.com/en-us/azure/synapse-analytics/machine-learning/tutorial-text-analytics-use-mmlspark

1. Speech to Text 2. Text to Speech

1. Speech to Text 2. Text to Speech

ChatGPT: speech to text + text to speech

The answer should be Text Analytics and TTS.

1. Speech to Text 2. Test to Speech From Takedajuku perspective, if you study for 4 days and spend 2 days reviewing, you will have a better chance of passing the exam.

Final Answer: 1. Speech to Text 2. Text to Speech

Language identification (LID) use cases include: Speech to text recognition when you need to identify the language in an audio source and then transcribe it to text. Speech translation when you need to identify the language in an audio source and then translate it to another language. https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-identification?tabs=once&pivots=programming-language-python

People, read the descriptions, these are describing only parts of the process, not the whole process. 1) DETECT incoming language has got not to do with speech to text, but with text analytics. Yes, speech to text is a part of the whole process but this services TRANSCRIBES and does not do language detection. 2) Respond to caller's language should be Text to speech

It can't be Text Analytics. It is an incoming call. It should be Speech to Text https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-identification?tabs=once&pivots=programming-language-csharp And then Text to Speech for the response

Speech to text - as it's based on a voice call Text to speech - it's not translator here, as the key thing is that the program needs to respond, not just translate

At this time, this question is out of date. See https://learn.microsoft.com/en-us/azure/ai-services/language-service/language-detection/overview . In old time, Text Analytics is correct.

1. Speech to Text 2. Test to Speech https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/language-identification?tabs=once&pivots=programming-language-csharp#speech-to-text You use Speech to text recognition when you need to identify the language in an audio source and then transcribe it to text. https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/text-to-speech Text to speech enables your applications, tools, or devices to convert text into humanlike synthesized speech. The text to speech capability is also known as speech synthesis. Use humanlike prebuilt neural voices out of the box, or create a custom neural voice that's unique to your product or brand.