嘈杂语噪声下汉语语句测听中的学习效应

目的 验证采用噪声下语句识别表进行言语测听时是否存在"学习效应"(learning effect),并确定在使用本测试材料时需要几张练习表才能抵消学习效应.方法 选取年龄18～25岁、听力/言语发育正常且日常以汉语普通话为交流方式的64名受试者,在-3、-5、-7、-9 dB四种信噪比(signal-to-noise ratio, SNR)条件下进行语句识别率测试.采用32张嘈杂语噪声下汉语普通话语句识别表(每表10句,每句3～7个     

嘈杂语噪声下汉语整句识别的同质性研究

目的 获得嘈杂语噪声背景下汉语短句识别率一信噪比(Performance-Intensity,P-I)函数的斜率和50%的得分所对应的信噪比(记为SNR50),并对所有短句的同质性进行评估和调整.方法 采用16张(每张20句)新编嘈杂语噪声下汉语普通话短句识别表作为测试材料.选取年龄为21～25岁之间、听力/言语发育正常且日常以汉语普通话为交流方式的48名受试者按"随机区组设计"在-1、-4、-7、-10 dB四种信噪比条件下进行言语识别率的心理声学测量.使用Statistica7.0软件进行P-I函数拟合和统计分析.结果 320句语句在嘈杂语噪声下的言语识别P-I函数曲线的阈值呈正态分布,斜率呈不规则分布.剔除P-I函数强健性增长(斜率＞55%/dB)的语句,并保留阈值变异度在±2σ内的语句,精选出同质性良好的240句.结论 逐个调整每句的SNR以实现同质性,可为编制"等言语识别阈级"的句表奠定扎实的心理声学基础.     

噪声下汉语语句识别表在视、听、视＋听三种模式下的等价性研究

目的验证新编中文普通话版嘈杂语噪声下语句识别表在视、听、视＋听三种模式下的等价性。方法招募27名听力正常人,采用二因素（视听模式、测试表号）重复测量实验设计,分别在单视觉．单听觉（65dB SPL言语强度、-6dB信噪比）及视听结合三种模式下,在声场中完成对27张嘈杂语噪声下语句识别表的言语识别率测试。三种视听模式及测试表号在所有受试者中均衡排布。结果综合三种视听模式的识别率,27张表的识别率无显著性差异（F=0．929,P=0．568）。视．听、视＋听三种模式下的语句识别率分别为33．4±14．1％、40．9±14．4％、91．4±7．3％,两两之间均表现出显著性差异。结论视觉信息的补充,可有效提高噪声下的语句感知能力。新编中文普通话版嘈杂语噪声下语句识别表在视。听、视＋听三种模式下均等价,可用于人工耳蜗植入疗效的评估。     

自适应噪声下语句识别阈算法的正常青年男性参考值

目的 验证自适应噪声下句子识别阈测试方法 的可靠性,获得自适应噪声下语句识别阈算法的正常青年男性参考值.方法 21名正常听力男性青年(平均年龄21±1.56岁)接受测试.采用嘈杂语噪声下汉语短句识别测听材料,使用自行编制的自适应算法经言语测听软件进行噪声下句子识别阈测试.受试者每人每耳各测试2表.使用u 检验将获得的平均识别阈SNR50a与所采用材料的整体识别阈SNR50进行比较.结果 自适应方法 测得噪声下平均句子识别阈SNR50a为(-5.75±0.92)dB.左右耳识别阈之间无统计学差异(P=0.0796).正常青年男性95%医学参考值范围为(-7.55 dB,-3.95 dB).使用自适应算法获得的噪声下句子识别阈略高于正常听力人群中通过多强度测试获得的识别阈(P<0.0001).结论 该自适应噪声下语句识别阈算法快速、可靠,为临床提供了一种评价噪声下言语识别能力的测听方法.     

唇读信息对正常人语句识别的影响

目的通过单视觉(唇读)、单听觉、视觉与听觉结合对正常青年人言语识别率的对比测试来说明唇读对语句识别的影响。方法选择正常听力青年大学生27名,分别在只看视频(唇读)、只听声音、视频和声音结合的情况下用新编中文普通话版嘈杂语噪声下语句识别表测试其言语识别率。结果在单视觉、单听觉和视觉与听觉结合的情况下,27例受试者平均言语识别率分别为33.1%±10.1%、40.8%±10.6%、91.5%±3.9%,视觉与听觉结合情况下的平均言语识别率显著高于单视觉、单听觉,差异有统计学意义(P<0.05)。结论在噪声环境下,唇读信息可明显提高正常人对语句识别的能力。     

噪声对低龄正常儿童言语感知的影响

目的探讨言语谱噪声(speech spectrum-shaped noise,SSN)和多人谈话噪声(babble noise,BN)对低龄正常儿童普通话词汇相邻性测试(Mandarin lexical neighborhood test,MLNT)言语感知的影响。方法 34例3~6岁正常听力儿童分为SSN组(21例)和BN组(13例),使用噪声下普通话词汇相邻性测试系统对两组儿童行声场下的言语测试,以听说复述方法获得不同信噪比下的言语识别率,比较两组两类噪声下的单音节易词表、难词表和双音节易词表、难词表的识别率-信噪比函数曲线(P-SNR曲线)和言语识别阈(SNR50)。结果 SSN组双音节易词表和难词表的SNR50阈值分别为-3dB、-0.5dB,单音节易词表和难词表的SNR50分别为-1dB、3.5dB;BN组双音节易词表和难词表的SNR50分别为-3dB、2dB,单音节易词表和难词表的SNR50分别为0.5dB、10dB。两组除双音节易词表SNR50相同外,其余各类词表BN组的SNR50均比SSN组高。词汇学因素对正常听力儿童噪声下的开放式言语识别的影响仍表现出易词识别率高于难词,双音节词识别率高于单音节词。结论对3~6岁正常听力儿童BN的掩蔽效应比SSN强,词汇学因素在噪声下仍然影响儿童的言语识别。     

感音神经性听力损失患者不同噪声模式下的言语识别能力研究

目的　研究不同噪声模式对听力正常者和感音神经性听力损失者言语识别能力的影响。方法　通过MATLAB软件播放儿童版汉语普通话噪声下言语测试句表,测试25名听力正常受试者和20例感音神经性聋受试者分别在安静环境、语谱噪声、双人语噪声下的言语识别能力,分析两者在安静和两种噪声模式下对语句感知能力的差异。结果　随信噪比降低,听力正常者在语谱噪声下言语识别率低于双人语噪声,但差异无统计学意义（P＞0.05）。感音神经性听力损失者在信噪比+5 dB时,双人语噪声下言语识别率低于语谱噪声,且差异有统计学意义（P＜0.05）。结论 　两种噪声模式对比,听力正常者在双人语噪声下言语识别率更高,感音神经性听力损失者在语谱噪声下言语识别率更高。     

听神经病患者噪声下言语测听临床应用与分析

目的对比听神经病患者安静与噪声下言语识别率的差异并与正常受试者、感音神经性听力损失组、听神经瘤组进行比较。方法测试在符合国家标准的隔声室内进行,纯音测试及言语测试应用校准后丹麦耳听美Conera听力计Otosuite(版本号4.82)联结计算机输出言语声,受试者佩戴头戴式耳机TDH-39、B71骨导振动器测试纯音。言语识别测试材料采用解放军总医院郗昕编制的《普通话言语测听—单音节识别测试》词表,在安静和噪声环境下,分别测试听神经病患者10例、感音神经性聋患者11例、听神经瘤患者11例和听力正常受试者10例患者在平均听阈、阈上10dB、20dB、30dB处的言语识别率以及信噪比为-0、-5、-10、-15dB的言语识别率得分。结果听神经病患者在噪声下言语识别能力明显低于听神经瘤组、感音神经性听力损失组以及正常听力组(P<0.05);具有相似听力阈值及听力曲线的AN患者,给予安静及不同噪声强度测试,可呈现较差及较好二级分化的SRS曲线;正常组在信噪比为-0、-5、-10、-15dB的环境下,信噪比为10dB时对比自身安静环境言语识别得分无显著性差异(P>0.05),而听神经病组、听神经瘤组和感音神经性听力损失组在-10 SNR处均有显著性差异(P<0.05)。听神经病患者在安静环境下随刺激声强度的升高会出现"回跌"现象。听神经病患者总体水平在安静与噪声环境下纯音听阈与言语识别得分均与无相关性(R2=0.07),其他三组呈现负的弱相关或强相关。结论安静环境下言语识别能力较好的听神经病患者在噪声环境中下降程度更为显著,相对于安静环境言语识别测试更加敏感;采用平均阈上30dB及-10dB信噪比测试,所得言语识别得分可作为临床评价言语功能的敏感指标,且对于听神经病诊断和病变定位及程度分析更具有现实意义,能够更全面评估听神经病患者的言语交流能力。     

噪声下言语识别速测表（Quick SIN）普通话版的编制

目的编制普通话版的噪声下言语识别速测表（QuickSpeech-In-Noise,Quick SIN）并进行等价性筛选。方法从嘈杂语噪声下的普通话儿童短句库中抽取90个句子组成15张噪声下旬表。每张表6句话,每句包含5个关键词,表中各句的信噪比依次为＋15,＋10,＋5,0,-5,-10dB。选择15名18～25岁听力正常人为受试者,每位受试者依次测试15张表,计算各自的信噪比损失。对15张句表的信噪比损失进行单因素方差分析,并使用TukeyHSD检验进行表间两两比较和等价性分析。结果15名受试者的信噪比损失为（0．84±1．77）dB。经统计学分析,15张句表中有12张表相互等价,表5、8、14不等价。单侧80％医学参考值范围是小于＋2．32dB,95％医学参考值范围是小于＋3。74dB。结论普通话版的Ouick SIN为临床提供了一种简单、快捷地评价患者在噪声下言语识别能力的测听方法,并为助听装置的选配提供了客观依据。     

人工耳蜗使用者噪声环境下言语识别能力研究

目的:评估人工耳蜗(CI)植入者在噪声环境下的言语识别能力。方法:利用普通话版噪声下言语测试(MHINT)为言语测试工具,选取22例CI受试者,按照MHINT适应性得分规则,首先确定受试者在噪声下识别50%～74%的句子内容(评分规则3),达到该水平则继续进行75%以上难度内容(评分规则2或规则1),否则停止。获得受试个体在噪声下言语识别能力的语句接受阈、言语识别率和PI function曲线。结果:22例受试者中,5例在噪声下识别能力达80%～90%,3例可达60%～70%,7例达到50%,7例未能达到MHINT评分规则3测试。获得15例受试者的PI function并与听力正常人进行比较。结论:MHINT适应性得分规则测试法,可有效地获得CI植入者在噪声下的言语识别能力。与听力正常者相比,CI植入者的PI function显示右移10dB以上,在噪声环境下仍需要更好的信噪比。     

Construction and evaluation of the Mandarin Chinese matrix (CMNmatrix) sentence test for the assessment of speech recognition in noise

Abstract

Objective: Development of the Mandarin Chinese matrix (CMNmatrix) sentence test for speech intelligibility measurements in noise according to the international standard procedure.

Design: A 50-word base matrix representing the distribution of phonemes and lexical tones of spoken Mandarin was established. Hundred sentences capturing all the co-articulations of two consecutive words were recorded. Word-specific speech recognition functions, speech reception thresholds (SRT: signal-to-noise ratio (SNR), that provides 50% speech intelligibility) and slopes were obtained from measurements at fixed SNRs. The speech material was homogenised in intelligibility by applying level corrections up to ± 2?dB. Subsequently, the CMNmatrix test was evaluated, the comparability of test lists was measured at two fixed SNRs. To investigate the training effect and establish the reference data, speech recognition was measured adaptively.

Study sample: Overall, the study sample contained 80 normal-hearing native Mandarin-speaking listeners.

Results: Multi-centre evaluation measurements confirmed that test lists are equivalent in intelligibility, with a mean SRT of ?10.1?±?0.1?dB SNR and a slope of 13.1?±?0.9 %/dB. The reference SRT is ?9.3?±?0.8 and ?11.2?±?1.2?dB SNR for the open- and closed-set response format, respectively.

Conclusion: The CMNmatrix test is suitable for accurate and internationally comparable speech recognition measurements in noise.     

多人谈话噪声及年龄对儿童言语识别中词汇学效应的影响

目的 探讨引入多人谈话噪声(babble noise,BN)后,词汇学效应在竞争性噪声环境下对正常听力儿童言语识别能力的影响及其随年龄增长的发展特性.方法 以212例3~6岁的正常听力儿童为研究对象,使用普通话词汇相邻性测试(Mandarin lexical neighborhood test,MLNT)材料(含双音节易词、双音节难词、单音节易词、单音节难词四类词表),在安静条件下给声强度70 dB SPL,多人谈话噪声下信噪比(signal to noise ratio,SNR)为4 dB,对所有对象进行开放式言语测试,分别获取四类词表在安静和噪声条件下的言语识别率,并比较3、4、5、6岁组儿童的结果.结果 在安静条件下正常听力儿童双音节易、难词及单音节易、难词的识别率分别为96.45%±5.17%、88.87%±7.73%、91.90%±7.31%、82.38%±7.95%,噪声下四类词表言语识别率分别为85.34%±11.23%、66.42%±11.08%、68.81%±15.99%、48.58%±12.81%,在噪声下的各词表言语识别率显著低于安静条件下(P<0.05).在安静和噪声条件下对词汇学效应的影响显示易词的识别率高于难词(P<0.05),双音节词言语识别率高于单音节词(P<0.05),且在噪声下比安静环境下影响更明显.噪声下不同年龄组中3岁组四类词表言语识别率分别为80.83%±12.65%、60.63%±9.13%、58.54%±12.98%、41.88%±11.69%,6岁组分别为92.38%±6.64%、71.90%±10.66%、76.90%±14.53%、57.14%±12.61%,3~6岁听力正常儿童噪声下言语识别能力随年龄增长显著提高(P<0.05).结论 在噪声下词汇学效应对听力正常儿童词汇提取的影响较安静环境下更明显,双音节词和易词更易获取,单音节词和难词更难获取;3~6岁听力正常儿童噪声下的言语识别能力随年龄增长而提高,且在6岁时仍未达到平台期.     

噪声频谱特性对正常听力成人词汇识别影响

目的　探讨噪声频谱特性对词汇识别的影响作用。方法　31名听力正常成人参与研究,研究采用汉语普通话词汇相邻性测试,分别获得言语谱噪声（speech spectrum-shaped noise,SSN）和多人谈话噪声（babble noise,BN）下不同信噪比（signal noise ratio,SNR）的言语识别率,并计算词汇识别率为20%、50%和80%时所需要SNR和斜率,进而分析噪声频谱特性对词汇识别的影响作用。结果　SSN条件下单音节词和双音节词的言语识别阈,即正确识别率为50%所对应的SNR（SNR50）分别为-4.83 dB和-7.63 dB（t 50=11.918,P＜0.05）,对应斜率分别为5.61和11.14（t 50=-7.006,P＜0.05)。BN条件下单音节词和双音节词的SNR50分别为-3.34 dB和-5.52 dB（t 50=8.860,P＜0.05),对应斜率分别为6.04和9.28（t 50=-4.316,P＜0.05）。统计显示SSN和BN下单、双音节词识别阈均有统计学差异（t 50M=-5.037,t 50D=-10.275,P＜0.05）,两类噪声下双音节词的斜率有统计学差异（t 50D=2.920,P＜0.05）。结论　BN噪声频谱掩蔽效应比SSN更强。在两种噪声下,单音节词均需要更高的SNR,才能达到和双音节词相同的词汇识别。     

Use of adaptive digital signal processing to improve speech communication for normally hearing and hearing-impaired subjects

A two microphone adaptive digital noise cancellation technique was used to improve word-recognition ability of normally hearing and hearing-impaired subjects in the presence of varying amounts of multitalker speech babble noise and speech spectrum noise. Signal-to-noise ratios varied from -8 dB to +12 dB in 4 dB increments. The adaptive noise cancellation technique resulted in reducing both the speech babble and speech spectrum noises 18 to 22 dB. This reduction in noise resulted in average improvements in word recognition, at the poorest signal-to-noise ratios, ranging from 37% to 50% for the normally hearing subjects and 27% to 40% for the hearing-impaired subjects. Improvements in word recognition in the presence of speech babble noise as a result of adaptive filtering were just as large or larger than improvements found in the presence of speech spectrum noise. The amount of improvement of word-recognition scores was most pronounced at the least favorable signal-to-noise ratios.     

老年性聋患者普通话快速噪声下言语测试及评估

目的评估老年性聋患者噪声下言语识别能力,探讨年龄、听力损失程度、认知功能对其噪声下言语识别能力的影响。方法选取2018年10月~2020年4月就诊的70例60岁及以上老年性聋患者为研究对象,按年龄分为60~69岁(20例40耳)、70~79岁(28例56耳)、≥80岁(22例44耳)三组,各组分别进行纯音听阈测试、简易智能精神状态量表(mini-mental state examination,MMSE)评估及普通话快速噪声下言语测试(Mandarin quick speech-in-noise test,M-Quick SIN),分别获得双耳0.5、1、2、4 kHz纯音平均听阈(pure-tone audiometry,PTA)、MMSE量表总得分及双耳信噪比损失(signal-to-noise ratio loss,SNR loss);分析年龄、平均听阈、MMSE量表得分对信噪比损失的影响。结果①60~69岁组SNR loss(5.25±5.42)dB明显小于70~79岁组(11.54±6.05)dB和≥80岁组(11.86±6.06)dB(P<0.01);70~79岁组和≥80岁组间SNR loss差异无统计学意义(P>0.05)。②SNR loss随PTA提高而升高,各组间差异均有显著统计学意义(P<0.01)。③MMSE量表得分对SNR loss的主效应不显著(P>0.05)。结论年龄、听力损失程度为老年性聋患者噪声下言语识别的主要影响因素;在一定范围内随着年龄增加,听力损失加重,其噪声下言语识别能力降低。     

Recognition of speech produced in noise.

A two-part study examined recognition of speech produced in quiet and in noise by normal hearing adults. In Part I 5 women produced 50 sentences consisting of an ambiguous carrier phrase followed by a unique target word. These sentences were spoken in three environments: quiet, wide band noise (WBN), and meaningful multi-talker babble (MMB). The WBN and MMB competitors were presented through insert earphones at 80 dB SPL. For each talker, the mean vocal level, long-term average speech spectra, and mean word duration were calculated for the 50 target words produced in each speaking environment. Compared to quiet, the vocal levels produced in WBN and MMB increased an average of 14.5 dB. The increase in vocal level was characterized by increased spectral energy in the high frequencies. Word duration also increased an average of 77 ms in WBN and MMB relative to the quiet condition. In Part II, the sentences produced by one of the 5 talkers were presented to 30 adults in the presence of multi-talker babble under two conditions. Recognition was evaluated for each condition. In the first condition, the sentences produced in quiet and in noise were presented at equal signal-to-noise ratios (SNR(E)). This served to remove the vocal level differences between the speech samples. In the second condition, the vocal level differences were preserved (SNR(P)). For the SNR(E) condition, recognition of the speech produced in WBN and MMB was on average 15% higher than that for the speech produced in quiet. For the SNR(P) condition, recognition increased an average of 69% for these same speech samples relative to speech produced in quiet. In general, correlational analyses failed to show a direct relation between the acoustic properties measured in Part I and the recognition measures in Part II.