Fluency issues in speech synthesis technology

Fluency issues in speech synthesis technology require specific code examples

With the development of artificial intelligence, speech synthesis technology has been widely used in various fields, such as virtual Assistant, driverless driving, etc. However, when using speech synthesis technology, we often encounter some problems with poor fluency, such as unnatural speaking speed, intermittent speech, etc. This article will discuss the fluency issue in speech synthesis technology in detail and give specific code examples.

First of all, one of the main causes of fluency problems is caused by text input. Sometimes, the text contains some long sentences, complex vocabulary or professional terms, which makes the speech synthesis system unable to process it accurately. To solve this problem, we can use text processing algorithms to split long sentences into shorter clauses or phonetic notate complex words. The following is a sample code using Python:

import nltk

def text_processing(text):
    sentences = nltk.sent_tokenize(text)  # 将文本分割为句子
    processed_text = ""
    for sentence in sentences:
        words = nltk.word_tokenize(sentence)  # 将句子分割为词语
        for word in words:
            phonetic = get_phonetic(word) # 获得词语的音标
            processed_text += phonetic + " "
    return processed_text

def get_phonetic(word):
    # 在这里编写获取词语音标的代码
    return phonetic

text = "我喜欢使用语音合成技术进行虚拟助手开发"
processed_text = text_processing(text)
print(processed_text)

In the above code, we use the Natural Language Toolkit (NLTK) library for text processing, segment the text into sentences, and segment and phoneticize each word Label. The specific function for obtaining phonetic symbols needs to be implemented according to the specific speech synthesis system and language processing library.

Secondly, the fluency issue is also related to audio processing. The audio generated by the speech synthesis system may sometimes be too long or too short, resulting in poor smoothness. In order to solve this problem, we can use audio processing algorithms to speed up or slow down the audio. The following is a sample code using Python:

from pydub import AudioSegment

def audio_processing(audio_path):
    audio = AudioSegment.from_file(audio_path, format="wav")
    audio = audio.speedup(playback_speed=1.2)  # 加速1.2倍
    audio.export("processed_audio.wav", format="wav")

audio_path = "original_audio.wav"
audio_processing(audio_path)

In the above code, we use the PyDub library for audio processing, load the audio file and accelerate it by 1.2 times, and finally export the processed audio file. Of course, the specific audio processing algorithm can be adjusted according to actual needs.

To sum up, the fluency problem in speech synthesis technology is an important issue of great concern and can be improved through algorithms such as text processing and audio processing. The above gives a code example using Python, but the specific implementation needs to be adjusted according to the actual situation. I hope the content of this article can be helpful in solving fluency problems.

The above is the detailed content of Fluency issues in speech synthesis technology. For more information, please follow other related articles on the PHP Chinese website!