Using TTS, we can convert digital text into spoken voice output. This process has become very important in many applications, providing critical accessibility options, enhancing user experience with chatbots, and much more. Microsoft Azure, with its robust AI solution, provides several tools and methodologies to enhance TTS. In this article, we will explore how to advance TTS using Speech Synthesis Markup Language (SSML) and Custom Neural Voice.
SSML: A Powerful TTS Tool
Speech Synthesis Markup Language (SSML) is a standardized language for controlling various aspects of synthesized speech output, such as pronunciation, pitch, rate, volume, etc. It can add a natural feel to the synthesized speech making it more engaging and effective for users.
For instance, using SSML, an event scheduled alert can be expressed using different voice attributes for each piece of information, making it easier for the user to comprehend.
Without SSML: “Event reminder. Lunch with Paul at 1 PM”
With SSML: “Event reminder. <prosody rate=’slow’ volume=’loud’> Lunch with Paul </prosody> at <prosody rate=’x-slow’> 1 PM.</prosody>”
Using Microsoft Azure’s Cognitive Services, TTS with SSML can be utilized as follows:
<code>
from azure.cognitiveservices.speech import SpeechConfig, SpeechSynthesizer, SpeechSynthesisOutputFormat, AudioConfig
import os
speech_config = SpeechConfig(subscription=”<subscription_key>”, region=”<region>”)
audio_config = AudioConfig(filename=”outputaudio.mp3″)
speech_synthesizer = SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config)
ssml_string = open(“ssml.xml”, “r”).read()
result = speech_synthesizer.speak_ssml_async(ssml_string).get()
print(result.reason)
</code>
This above code will generate an output audio file that represents the text enhanced by SSML.
Custom Neural Voice: A Human-like TTS model
Echoing human speech with customized human-like voice digitally is a significant transformation that Azure AI’s Custom Neural Voice offers. It’s a Text-to-Speech feature enabling the creation of a unique, recognizable and natural sounding voice model from the training data.
With Custom Neural Voice model, you can control:
- Voice Signature: Personalize the voice’s pitch, style, and speaking speed.
- Emotional Tone: Add emotions to text-to-speech outputs.
This model can improve user engagement and offer a more personalized service. For instance, a virtual assistant for a specific brand can have its own unique voice, which will be recognized by the user as the brand’s voice.
The following steps summarize the process to create your own Custom Neural Voice model in Azure:
- Plan your voice talent: Select the speaker who will record voice samples for the model.
- Record your datasets: Record enough sentences to cover a full range of phonetics.
- Train your voice model: Follow the Microsoft Azure AI documentation to train your model.
- Test the model: Ensure the model is working as desired.
Remember, using Custom Neural Voice, you must comply with responsible AI practices. Using someone’s voice without consent is strictly prohibited.
In conclusion, the combined use of SSML and Custom Neural Voice offers enhanced control over TTS outputs, allowing for pronounced emotional depth, aesthetics, uniqueness, and improved user engagement. Both these technologies are significant aspects of Azure AI-102 exam and for anyone aiming at designing and implementing a Microsoft Azure AI solution, in-depth knowledge of these aspects is crucial.
Practice Test
True or False: SSML stands for Speech Synthesis Markup Language.
- True
- False
Answer: True
Explanation: SSML is indeed an abbreviation for Speech Synthesis Markup Language. It is a standardized language for controlling speech output, such as volume, rate, pitch, pronunciation and so on.
Which of the following is NOT a capability of SSML in improving text-to-speech quality?
- A) Altering the speed of speech
- B) Changing the voice pitch
- C) Altering the text content
- D) Managing pauses in speech
Answer: C) Altering the text content
Explanation: SSML can enhance text-to-speech output by controlling the rate, volume and pitch of speech, and managing pauses, but it cannot alter the text content itself.
True or False: Custom Neural Voice can only use pre-recorded voices for text-to-speech conversion.
- True
- False
Answer: False
Explanation: Custom Neural Voice actually allows you to create a unique, synthetic voice model of a particular speaker based on their speech recordings, along with their written text transcripts.
In context of SSML, what does the ‘prosody’ element represent?
- A) Speed of speech
- B) Voice pitch
- C) Both A and B
- D) None of the above
Answer: C) Both A and B
Explanation: The ‘prosody’ element in SSML is used to control attributes of speech such as speed (rate), pitch, and volume.
In Azure’s Custom Neural Voice, which of the following can be used for voice training data?
- A) Only text data
- B) Only audio data
- C) Both text and audio data
- D) None of the above
Answer: C) Both text and audio data
Explanation: To create Custom Neural Voice, both high-quality audio recordings and their corresponding transcripts are required.
True or False: SSML allows a user to specify whether the voice should sound happy, sad, urgent, etc.
- True
- False
Answer: False
Explanation: While SSML does allow a user to control aspects of speech such as speed, pitch and volume, it does not have the capability to specify emotional tones.
What are the main input components used for training a Custom Neural Voice?
- A) Text data
- B) Audio data
- C) Both A and B
- D) None of the Above
Answer: C) Both A and B
Explanation: Custom Neural Voice relies on a significant amount of high quality speech data, and corresponding written text to create a synthetic voice.
True or False: You can use SSML to optimize the pronunciation of specific words and phrases in text-to-speech.
- True
- False
Answer: True
Explanation: SSML has specific tags that allow users to customize the pronunciation of words or phrases, enhancing the quality of text-to-speech output.
Which tool in Azure AI can help in creating a unique, synthetic voice model?
- A) SSML
- B) Custom Neural Voice
- C) Azure Machine Learning
- D) Azure Bot Service
Answer: B) Custom Neural Voice
Explanation: Custom Neural Voice in Azure AI lets you build a unique, synthetic voice model, based on a speaker’s voice recording and corresponding textual transcription.
True or False: SSML is only applicable to English language speech synthesis.
- True
- False
Answer: False
Explanation: SSML is a language independent standard for controlling various aspects of synthesized speech, and can be used with many different languages.
Interview Questions
What does SSML stand for in the context of enhancing text-to-speech capabilities?
SSML stands for Speech Synthesis Markup Language. It is a markup language that provides a rich, XML-based language for assisting the generation of synthetic speech in web and other applications.
What is Custom Neural Voice in Azure?
Custom Neural Voice is an Azure service feature that allows developers to build a unique voice, starting with a few minutes of training audio.
How can SSML enhance the performance of text-to-speech engines?
SSML can be used to improve text parsing and pronunciation, thereby enhancing the output of text-to-speech systems. It allows for fine-tuning of pronunciation, volume, pitch, rate or speed, emphasis, and other aspects of voice quality.
How can SSML improve the quality of synthesized speech in Custom Neural Voice?
With SSML, you can add breaks, emphasize specific words, or change the speech rate, volume, or voice pitch. This is helpful in creating a more human-like, dynamic, and expressive voice with Custom Neural Voice.
How does Custom Neural Voice work?
It uses deep neural networks and other techniques to create a unique and lifelike voice identity. It is trained using a dataset of speech samples and the corresponding texts.
What are some tags used in SSML to enhance speech synthesis?
Some commonly used SSML tags include <break> for silence, <emphasis> to add stress, <prosody> to adjust pitch, speed, and volume, and <phoneme> to handle specific pronunciations.
What are some use cases for Custom Neural Voice?
Use cases include creating voice assistants, reading of digital books, voice-overs for digital content, or as a personalized customer service representative in a call center.
How can you change the speaking style of a text using SSML in Azure’s text-to-speech service?
This can be done using the <prosody> tag to adjust the speech attributes. For example, <prosody rate=”slow”> will slow down the speech rate.
Is it possible to add pauses in the synthesized speech using SSML? If so, how?
Yes, a pause can be added using the <break> tag. By specifying the length attribute inside this tag, you can dictate the duration of the pause.
How does the Custom Neural Voice offer security and privacy for the voice data used for training?
Azure follows strict privacy and security guidelines, ensuring the data used for training Custom Neural Voice is securely encrypted and only used with explicit user permission.
Can you create multiple synthetic voices with a single Custom Neural Voice model?
No, each training of a model in Custom Neural Voice creates a single synthetic voice.
How can tone be added to synthesized speech using SSML?
The <prosody> tag in SSML can be used to alter the pitch, volume, and rate of the spoken text, effectively adding different tones to the synthesized speech.
What is the role of Azure in developing Custom Neural Voice solutions?
Azure Cognitive Services provides the necessary infrastructure and services to train, test, and deploy Custom Neural Voice solutions.
How can SSML control speech pronunciation in text-to-speech synthesis?
By using the <phoneme> tag in SSML, developers can specify the exact pronunciation for any word, which is especially useful for names, locations, or other special terms.
What kind of data is required to train a Custom Neural Voice model on Azure?
Custom Neural Voice requires audio data and the matching transcribed text. The audio data, commonly collected from voice talents, is used to create a unique voice font.
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