根据基于双门限法、相关法、谱熵法等的语音端点检测原理，编写程序实现语音信号的端点检测函数，函数输出参数包括：voiceseg是一个数据结构，记录了语音端点的信息；vsl是voiceseg 的长度；SF是语音帧标志（SF=1表示该帧是语音段）；NF是噪声/无声帧标志（NF=1表示该帧是噪声/无声段）

以下是一个简单的语音端点检测函数的示例代码：

import numpy as np
from scipy.signal import butter, lfilter

def butter_bandpass(lowcut, highcut, fs, order=5):
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    b, a = butter(order, [low, high], btype='band')
    return b, a

def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
    y = lfilter(b, a, data)
    return y

def endpoint_detection(signal, frame_len, frame_step, win_len, threshold=0.05):
    # Pre-emphasis
    signal = np.append(signal[0], signal[1:] - 0.97 * signal[:-1])

    # Framing
    signal_length = len(signal)
    frame_length = int(round(frame_len * fs))
    frame_step = int(round(frame_step * fs))
    num_frames = int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step))
    
    pad_signal_length = num_frames * frame_step + frame_length
    z = np.zeros((pad_signal_length - signal_length,))
    pad_signal = np.append(signal, z)
    
    indices = np.tile(np.arange(0, frame_length), (num_frames, 1)) + np.tile(np.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T
    frames = pad_signal[indices.astype(np.int32, copy=False)]
    
    # Windowing
    frames *= np.hamming(frame_length)
    
    # Feature extraction
    mag_frames = np.abs(np.fft.rfft(frames, win_len))
    pow_frames = ((1.0 / win_len) * ((mag_frames) ** 2))
    
    # Apply band-pass filter
    filtered_frames = butter_bandpass_filter(pow_frames, 300, 3000, fs, 6)
    
    # Compute short-term average energy
    energy = np.sum(filtered_frames, axis=1)
    energy /= np.max(energy)
    
    # Set threshold
    threshold *= np.average(energy)
    
    # Find endpoints
    endpoints = np.where(energy > threshold)[0]
    endpoints_diff = np.diff(endpoints)
    endpoints_diff = np.insert(endpoints_diff, 0, endpoints[0])
    endpoints_diff = np.append(endpoints_diff, len(signal) - endpoints[-1])
    endpoints = endpoints[np.where(endpoints_diff > 0.1 * fs)[0]]
    endpoints_diff = np.diff(endpoints)
    endpoints_diff = np.insert(endpoints_diff, 0, endpoints[0])
    endpoints_diff = np.append(endpoints_diff, len(signal) - endpoints[-1])
    endpoints = endpoints[np.where(endpoints_diff > 0.1 * fs)[0]]

    # Generate output
    voiceseg = np.zeros((2, len(endpoints)))
    voiceseg[0, :] = endpoints / float(fs)
    voiceseg[1, :] = np.diff(np.append(endpoints, signal_length)) / float(fs)
    vsl = len(endpoints)
    
    SF = np.zeros((num_frames,))
    SF[endpoints] = 1
    
    NF = np.zeros((num_frames,))
    NF[np.where(SF == 0)] = 1
    
    return voiceseg, vsl, SF, NF

其中，`signal` 是输入信号，`frame_len` 和 `frame_step` 分别是帧长和帧移（单位为秒），`win_len` 是 FFT 窗口长度，`fs` 是采样率。函数返回值 `voiceseg` 是一个 2xN 的矩阵，其中第一行是语音段的起始时间，第二行是语音段的时长（单位为秒）；`vsl` 是语音段的数量；`SF` 是语音帧标志，`NF` 是噪声/无声帧标志。